🧠 SymFSM Compatibility and Choosing the Optimal AI Model for Your Tasks

SymFSM works as a cognitive overlay on top of any language model, transforming plausible generation into verifiable reasoning. The choice of a specific LLM determines budget, speed, and specialization, while SymFSM ensures logical accuracy of the result regardless of how powerful the underlying model is.

Below is a complete list of compatible models with application recommendations: for each, we specify in which scenarios SymFSM provides maximum quality gain and for which tasks it is best suited.

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🔷 OpenAI Models — from Flagship Agentic Scenarios to Open-Source Solutions

• GPT-5.5 — OpenAI’s newest flagship with agentic architecture. When solving multi-step engineering tasks, the model tends to lose coherence between stages. SymFSM builds a cognitive map before launching the agent, checks the reachability of each step, and prunes unreachable branches before code generation.

• GPT-5.5 Pro — enhanced version with deep reasoning. Problem: high token cost does not guarantee logical accuracy. SymFSM in verification mode checks every output with automata, dramatically reducing the cost of errors.

• GPT-5.4 Nano — the smallest and cheapest model in the line. The main risk is superficial responses due to limited capacity. SymFSM compensates for this: the cognitive map structures the query so that even a weak model produces meaningful results within the given framework.

• GPT-5.4 Mini — fast model for bulk queries. During streaming processing of thousands of identical requests, logical errors accumulate unnoticed. SymFSM automatically validates the coherence of each response, turning the mass pipeline into a controlled process.

• GPT-5.4 — universal model with 1M token context and code handling capabilities. Large context causes “attention dilution” — the model mixes facts from different parts of the document. SymFSM captures entities and their connections in a graph, preventing contextual noise from destroying output structure.

• GPT-5.4 Pro — enhanced version for precision. In legal or technical document analysis, the cost of a missed contradiction is unacceptable. SymFSM runs formal reachability checks on the requirement graph and marks unreachable nodes before the model starts generation.

• GPT-5.3 Chat — chat version for communication and assistants. In dialogue systems, context accumulation often leads to the bot contradicting its own previous responses. SymFSM tracks dialogue statements as automaton states and prevents transitions to contradictory states.

• GPT-5.3 Codex — agentic model for programming and debugging. During code generation, the agent may miss edge cases or violate invariants. SymFSM checks the reachability of all requirements before the agent runs and offers repair patches for problematic areas.

• GPT-5.2 — universal model for text, code, and agentic tasks. Broad profile means the model lacks innate specialization and may choose suboptimal reasoning strategies. SymFSM identifies the task type and forcibly directs thinking along analytical, inventive, or verification trajectories.

• GPT-5.2 Pro — version with enhanced reasoning quality. On creative tasks, enhanced logic sometimes suppresses unconventional but promising ideas. SymFSM runs parallel thinking programs — both analytical and inventive — and selects the best, preserving both rigor and creativity.

• GPT-5.2 Chat — chat version for dialogues and assistants. In customer service, the bot tends to “fill in” instructions when the knowledge base lacks an exact answer. SymFSM acts as a safety net: if a node in the knowledge map is unreachable, the repair mechanism triggers a clarifying query instead of generating a fantasy.

• GPT-5.2 Codex — for programming and debugging. Test generation for written code often suffers from “blind spots” — the model checks only what it generated itself, missing alternatives. SymFSM builds a graph of all possible execution paths and finds trajectories not covered by tests.

• GPT-Audio — voice model for recognition and synthesis. During voice input, recognition errors cascade through subsequent analysis. SymFSM checks the coherence of extracted entities before passing text to the LLM and requests clarification when gaps are found, preventing noise from becoming part of the reasoning.

• GPT-Audio-Mini — compact voice model. Limited recognition accuracy on domain-specific terminology (medicine, engineering) leads to critical errors. SymFSM cross-references terms with the cognitive map of the domain and marks unrecognized ones as needing confirmation.

• GPT-5.1 — base model for a wide range of tasks. Low cost encourages use in scenarios beyond its capacity, producing systematically weak results. SymFSM splits complex tasks into reachable subtasks for this model and assembles results from verified parts.

• GPT-5.1 Chat — chat version for communication and support. In technical support, escalation of complex queries is often delayed — the bot attempts to answer and makes mistakes. SymFSM assesses query complexity by entity count and graph connections and determines in advance whether the model can produce a correct answer.

• GPT-5.1 Codex-Max — extended Codex model for complex code. During legacy code refactoring with implicit dependencies, the model misses hidden connections. SymFSM builds a dependency graph, identifies undocumented connections, and checks whether proposed changes would break other modules.

• GPT-5.1 Codex-Mini — lightweight Codex model for quick tasks. Tends to produce syntactically correct but logically flawed code on specific queries. SymFSM checks code compliance with given constraints before it reaches the editor.

• GPT-5 Nano — the cheapest model in the GPT-5 family. Extreme token economy encourages use in scenarios requiring deep analysis — with predictably low quality. SymFSM makes this model useful: the cognitive map takes on the structure, while the LLM handles only final generation based on the ready-made framework.

• GPT-5 Mini — fast and affordable model for bulk queries. Speed comes at the cost of depth — complex causal chains are reduced to simple patterns. SymFSM retains the full reasoning structure in the graph and uses the model only for articulating the found conclusions.

• GPT-5 — flagship model for universal tasks and code. Universality creates a strategy selection problem: which thinking mode to apply to a specific task? SymFSM runs competing thinking programs and selects the winner — the model receives not just a query, but a complete cognitive trajectory.

• GPT-5 Pro — version with maximum answer quality. For enterprise analytics, not only accuracy but also verifiability of each step is crucial. SymFSM reveals the entire reasoning chain from input data to conclusion, allowing auditors to reproduce the path and verify the absence of logical gaps.

• GPT-4.1 Nano — ultra-compact model with 1M token context. A million tokens of context with modest model capacity creates an illusion of depth — the model “sees” the entire document but cannot maintain complex connections. SymFSM compresses context into a structural graph, focusing the model only on relevant nodes.

• GPT-4.1 Mini — fast model with large context. Good for document search but poor for synthesis — it finds a relevant fragment but misses its contradictions with the rest of the text. SymFSM checks the found fragment for compatibility with the entire document map before including it in the answer.

• GPT-4.1 — strong model with 1M context for code and text. When analyzing monolithic codebases, the model “drowns” in volume, failing to identify architectural boundaries. SymFSM segments code into modules in the graph, builds connections, and checks change consistency at the architecture level.

• GPT-OSS-Safeguard-20B — open 20B model with moderation. In sensitive local deployments, it’s critical that the safety filter doesn’t produce false positives on legitimate queries. SymFSM checks whether the query leads to forbidden conclusions by structure, not by keywords.

• GPT-OSS-20B — open 20B model for local deployment. The main pain of open-source models is instability on tasks requiring multi-step reasoning. SymFSM replaces the model’s unstable internal logic with formal graph-based reasoning: the model receives a ready-made trajectory rather than building it itself.

• GPT-OSS-120B — open 120B model for complex tasks. Deployment requires significant resources, and when reasoning fails, the investment doesn’t pay off. SymFSM improves the large model’s efficiency: every thousand tokens works on a verified structure rather than iterative hypothesis generation.

• GPT-4o-Mini — fast and affordable model for everyday tasks. Optimal for simple queries, but users tend to overestimate its capabilities and send complex analytical tasks. SymFSM assesses cognitive complexity and, if it exceeds the model’s threshold, recommends switching to a more powerful model.

• GPT-4o-Mini-Search-Preview — with built-in web search. The combination of search and generation creates a problem: the model may accept an unreliable source as authoritative and build conclusions on it. SymFSM tags sources in the cognitive map as verified or unverified and does not draw conclusions from unverified data.

• GPT-4o-Search-Preview — GPT-4o with built-in search. Extended search results often contain contradictory facts. SymFSM detects contradictions in the fact graph and triggers repair mechanisms for conflict resolution before generating the answer.

• GPT-4o — multimodal model for text and images. Image analysis suffers from phantom details — the model “sees” what isn’t actually in the picture and embeds it into the textual output. SymFSM in Anti-Fantasy mode filters out entities lacking pixel-level confirmation and prevents them from entering the final answer.

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🔷 Google Models — from Agentic Gemini 3.5 Flash to Open Gemma

• Gemini 3.5 Flash — the newest multimodal model for agents and code. Lightning speed encourages use in streaming agent systems where a single error early on cascades and destroys the entire plan. SymFSM checks the graph of reachability for all goals before launching the agent chain and blocks trajectories with unachievable transitions, preventing cascading failures.

• Gemma 4-26B-A4B-IT — open MoE (Mixture of Experts) model, efficient and fast. MoE architecture delivers high speed at the cost of knowledge fragmentation between experts — the model may produce an answer where different parts contradict each other. SymFSM, working as an overlay on Gemma 4-26B-A4B-IT, checks the coherence of all answer parts in a single graph before the user sees the result.

• Gemma 4-31B-IT — open model for local deployment. Local deployment means no cloud backup — the model’s error is the system’s error. SymFSM works as a local verifier: every output undergoes formal reachability checking on your hardware, without sending data externally.

• Gemini 3.1 Flash-Lite — lightweight and fast model for scale. Designed for millions of cheap queries, where quality is often sacrificed for speed. SymFSM automatically identifies queries critical to accuracy (finance, medicine, legal) and enhances them with additional verification cycles without slowing down simple requests.

• Gemini 3.1 Flash-Lite Preview — preview of the lightweight version. Using preview versions in production environments carries a risk of behavioral instability. SymFSM remembers patterns of successful and unsuccessful reasoning for this model and, when a dangerous pattern repeats, suggests switching to a stable version or changes the cognitive strategy.

• Gemini 3.1 Pro Preview — powerful model for complex reasoning. The downside of power is excessive confidence in erroneous conclusions — the model argues for an incorrect answer so convincingly that the user accepts it. SymFSM prevents the model from “convincing” in unverified claims: formal verification of conclusion reachability operates independently of the LLM’s rhetorical capabilities.

• Gemini 3.1 Pro Preview CustomTools — version for custom tools. Connecting custom tools creates a risk of contract violation — the model may call a function with invalid parameters or in the wrong order. SymFSM checks pre- and post-conditions for each tool call in the execution graph before actual execution.

• Gemini 3 Flash Preview — fast model for visual and agentic tasks. Visual agents operating in real-time cannot afford long deliberation when detecting anomalies. SymFSM pre-builds a map of all known system states so that when an abnormal situation is detected, it doesn’t run a full analysis cycle but instantly transitions along the predetermined trajectory.

• Gemini 2.5 Flash-Lite — the most economical model in the 2.5 line. Extreme cost savings on tokens encourages use in scenarios requiring depth — with predictably weak results. SymFSM redistributes the load: builds the task graph using automata (without consuming tokens), and the model receives a structured query it can correctly answer.

• Gemini 2.5 Flash — fast model for everyday tasks. When automating document workflows, the model extracts entities well but fails to see conflicts between different documents. SymFSM builds a unified entity graph from the entire document package and identifies contradictions before they become the basis for business decisions.

• Gemini 2.5 Pro — powerful model for complex reasoning. In strategic sessions, it generates many scenarios but doesn’t check their mutual compatibility — resulting in a beautiful but unexecutable plan. SymFSM checks each scenario for reachability, prunes incompatible combinations, and leaves only executable trajectories.

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🔷 Anthropic Models — from Opus for Code to Haiku for Speed

• Claude Opus 4.8 — the most powerful model for code and agents. Power provokes excessive solution complexity — the model generates architecturally overloaded code that is hard to maintain. SymFSM introduces complexity constraints into the graph and rejects trajectories with redundant nodes, directing generation toward maintainable code.

• Claude Opus 4.8 Fast — accelerated version with the same power. Acceleration often comes at the cost of reduced internal reasoning cycles, which is critical for tasks where a missed logic step leads to an incorrect conclusion. SymFSM does not rely on the model’s internal cycles — it builds the full reasoning chain using automata and passes the ready-made framework to the model, saving its computational resources.

• Claude Opus 4.7 — top-tier model for the most complex tasks. When analyzing risks, the model tends to focus on the most probable threats, missing rare but catastrophic scenarios. SymFSM forcibly explores the entire trajectory space, including unlikely ones — and prevents the model’s optimism from masking “black swans.”

• Claude Sonnet 4.6 — balanced model for code and agents. The balance of speed and quality means compromise: on some tasks the model is fast but inaccurate, on others the opposite. SymFSM, analyzing cognitive complexity, dynamically determines whether the model’s depth is sufficient and, if necessary, includes additional verification cycles.

• Claude Haiku 4.5 — fast and affordable model for simple tasks. Designed for simple scenarios, but users systematically send complex queries — and receive “smooth” but incorrect answers. SymFSM acts as a “fool-proof” mechanism: if the task graph contains more than N nodes with non-trivial connections, the system warns about the risk and suggests a more powerful model.

• Claude Sonnet 4.5 — reliable model for code, text, and analysis. On cross-disciplinary tasks (e.g., bioinformatics), the model loses coherence across domains — the biological and algorithmic parts of the answer may contradict each other. SymFSM builds a unified graph with nodes from different domains and checks the coherence of the entire structure.

• Claude Opus 4.6 — powerful model for analytics and agents. Long analytical chains provoke “drift” — each step deviates slightly from the previous one, and by the end the answer contradicts the initial premises. SymFSM captures premises as the initial state and does not allow transitions into states incompatible with the original axioms.

• Claude Opus 4.5 — previous flagship for complex code. Using previous-generation models is justified by price, but the risk is that known bugs and limitations of this version remain. SymFSM remembers failure patterns for different model versions and, when a dangerous pattern is detected, changes the reasoning strategy or highlights the risk to the user.

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🔷 DeepSeek Models — Open Reasoners with 1M Context

• DeepSeek V4 Flash — lightweight version, very fast and cheap. During streaming processing of millions of tokens per day, even rare errors accumulate into absolute defect numbers. SymFSM automatically validates each response, and when a logical gap is detected, sends the request for regeneration with a refined cognitive map.

• DeepSeek V4 Pro — flagship with 1M context, top-tier open reasoner. The open architecture allows customization, but customization often breaks internal reasoning invariants. SymFSM, working on top of DeepSeek V4 Pro, does not depend on internal model changes — it builds its own consistent structure and uses the model only as an interpreter.

• DeepSeek V3.2 — proven MoE model with balanced price and quality. Proven track record creates a false sense of security — “this model has never failed on these tasks.” SymFSM does not rely on past success statistics — every response undergoes formal reachability verification on the current task graph.

• DeepSeek R1-0528 — open reasoning model for math and code. Specialization in reasoning creates a problem: the model attempts to apply mathematical rigor to tasks where loose analogy or creative leaps are more important. SymFSM identifies the task type and, when necessary, switches the mode from analytical to inventive, not allowing specialization to become a limitation.

• DeepSeek Chat V3.1 — fast chat model for bots and assistants. Response speed in chat systems often comes at the cost of losing long dialogue context. SymFSM stores dialogue history as a state graph and updates the entire structure with each new request, not allowing the model to “forget” what was agreed ten messages ago.

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🔷 Meta (Llama) Models — Open Solutions with Large Context

• Llama 4 Scout — compact model from Meta. Compactness encourages deployment on resource-constrained devices where no additional verifier can run. SymFSM performs heavy cognitive work using automata, consuming orders of magnitude fewer resources than the language model, leaving only final generation to the device.

• Llama 4 Maverick — model with 1M context. A million tokens of context in an open-source model is a powerful tool for documentation analysis, but without external control the model “drowns” in details. SymFSM builds a hierarchical document graph — from sections to paragraphs, from paragraphs to statements — and guides the model through this graph, preventing loss of focus.

• Llama 3.3 70B Instruct — open model for instructions and dialogues. 70 billion parameters require serious hardware, and every incorrect answer wastes computational resources. SymFSM improves model utilization: checks query reachability before generation and does not run expensive inference for obviously incorrect or incomplete queries.

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🔷 Perplexity Models — Search-Oriented Solutions

• Sonar — with web search. The combination of search and generation creates a unique risk: the model finds outdated or disproven information and presents it as current. SymFSM tags timestamps of sources in the graph and, when a contradiction between old and new facts is detected, triggers re-verification.

• Sonar Pro — for accurate answers with sources. Accuracy here means citability, not logical consistency — you can provide an exact quote that contradicts another exact quote. SymFSM checks not sources individually, but the entire picture — is the reasoning goal reachable while accepting all found facts simultaneously?

• Sonar Pro Search — with extended web search. Extended search means more sources, and more sources mean more potential contradictions. SymFSM scales the fact graph linearly with the number of sources and detects conflicts in time proportional to the number of nodes, not their combinations.

• Sonar Reasoning Pro — for complex reasoning. Combines fact search and reasoning chains, but these two phases are poorly coordinated: reasoning may rely on a fact that has been disproven by the time of the next search. SymFSM captures a “snapshot” of the fact graph at the start of reasoning and checks whether the factual base has changed by the time of the conclusion.

• Sonar Deep Research — for deep analysis. Deep analysis means dozens of search and synthesis iterations, with minor inaccuracies accumulating at each step and compounding into a major error by the end. SymFSM checks after each iteration whether the invariants set at the start of the research have been violated, and when drift is detected, returns the analysis to the branching point.

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🔷 xAI (Grok) Models — Agentic Solutions with Large Context

• Grok Build 0.1 — for development and agentic tasks. Early version for development means API and behavioral instability — code written today may stop working tomorrow. SymFSM abstracts the cognitive layer from the specific model: changing the Grok Build version does not require rewriting logic — only redirecting calls.

• Grok 4.3 — for text and code. Good at generation, weak at verifying its own output — a typical problem for models optimized for “helpfulness” rather than “correctness.” SymFSM adds the missing verification loop: after generation, automata check whether the output matches the original requirements in the task graph.

• Grok 4.20 — with 2M context. Two-million token context enables analysis of entire codebases or document libraries, but without a structured approach the model can’t distinguish important from secondary. SymFSM builds a significance graph: entities are ranked by number of connections, and the model receives context filtered from noise.

• Grok 4.20 Multi-Agent — for multi-agent scenarios. Multi-agent capability on a single model creates an illusion of independent decision-making, while in reality all “agents” inherit the same cognitive biases from the base model. SymFSM runs a separate thinking program for each agent and cross-checks their outputs, identifying consistency not at the text level but at the structure level.

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🔷 Qwen Models — Wide Lineup from Compact to Flagship MoE

• Qwen3.7 Plus — flagship for complex tasks and reasoning. Flagship status creates inflated expectations — users send the model tasks requiring not so much intelligence as specialized knowledge. SymFSM identifies the task domain and, if the model lacks relevant experience in that domain, restructures the strategy relying on formal logic rather than expert knowledge.

• Qwen3.7 Max — maximum version for demanding tasks. Maximum power means maximum token cost — and maximum cost of failure. SymFSM acts as an “investment safeguard”: before launching expensive generation, it checks that the query is correct, complete, and reachable — not wasting budget on obviously defective requests.

• Qwen3.6 Flash — fast and affordable model. In real-time scenarios (financial quotes, system monitoring), speed is everything — but speed without control means an incorrect answer reaches the consumer before anyone can notice. SymFSM performs verification in parallel with generation and blocks delivery when a logical gap is detected.

• Qwen3.5 Plus (2026-04-20) — strong universal model. Model versioning means behavior can change with updates — prompts that worked on the previous version yield different results. SymFSM captures the expected answer structure in the graph and, when it’s violated, signals the need to adapt prompts for the new model version.

• Qwen3.6 Max Preview — preview of the top-tier reasoning model. Using previews in production environments means balancing the desire for cutting-edge features with the risk of instability. SymFSM creates a “sandbox” for the preview model: all its outputs undergo double verification before entering the production pipeline.

• Qwen3.6 35B A3B — open MoE model. Mixture of Experts architecture provides speed advantages but creates a risk of “forgetfulness” — an expert activated at the start of a dialogue may not be activated later, losing context. SymFSM stores the entire interaction history in the graph and passes relevant nodes to the model regardless of which expert is activated.

• Qwen3.6 27B — open model for local deployment. 27 billion parameters is a reasonable compromise for local deployment, but on complex multi-step reasoning the model starts “cutting corners.” SymFSM doesn’t allow the model to skip steps: the reachability graph explicitly requires each intermediate node to be confirmed.

• Qwen3.5 9B — compact open model. An extremely compact model is attractive for embedding in applications, but it can easily be pushed into producing syntactically correct but semantically empty outputs. SymFSM checks that every node in the model’s answer has mechanical justification in the task graph and rejects empty wrappers.

• Qwen3.5 122B A10B — MoE model. Large MoE architecture delivers impressive benchmark results, but on real-world tasks with uneven domain distribution, some experts become overloaded while others sit idle. SymFSM distributes subtasks across cognitive domains evenly, not allowing the model to “drift” into a single thinking mode.

• Qwen3.5 Flash (02-23) — fast model with 1M context. Speed plus large context is a dangerous combination — the model quickly reads a million tokens and just as quickly makes an incorrect conclusion. SymFSM slows the process just enough to build and verify the graph — not delaying simple queries but blocking hasty conclusions on complex ones.

• Qwen3.5 35B A3B — open MoE from previous generation. Using previous-generation models is cost-effective but requires compensating for the increased error rate. SymFSM for legacy models automatically includes additional verification cycles, compensating for the base model’s quality decline with enhanced control.

• Qwen3.5 27B — open model for local deployment. In corporate environments, fine-tuning is valuable, but fine-tuning often breaks general reasoning ability for narrow specialization. SymFSM does not depend on fine-tuning — it builds logical structure using automata, and the model merely fills it with language.

• Qwen3.5 Plus (02-15) — version from February 2025. Previous model versions may have known vulnerabilities or bugs already fixed in newer releases, but your system still runs the old one. SymFSM maintains a database of known failure patterns for each version and, when a dangerous pattern is detected, warns about the risk or automatically changes the strategy.

• Qwen3.5 397B A17B — MoE model for complex tasks. A model of this scale is a serious infrastructure investment, and every minute of downtime or error costs money. SymFSM acts as a dispatcher: checks the query before it reaches the model and prevents expensive hardware from processing obviously incorrect or unreachable tasks.

• Qwen3 235B A22B (07-25) — MoE model from July 2025. Models with dated names create a problem: users may not know that three new versions have been released since July and continue using the outdated one. SymFSM checks the model version against the current list and informs the user about available updates.

• Qwen3 30B A3B — compact MoE. Compact MoE architecture performs well on tasks where one or two domains are active, but fails when the task requires simultaneous consideration of knowledge from many areas. SymFSM, when detecting a multi-domain query, splits it into a sequence of subtasks, each solved within its own domain.

• Qwen3 Coder — for programming. A code-specialized model generates correct syntax but may propose an architecturally incorrect solution — the code works but doesn’t solve the business problem. SymFSM checks code compliance not only with the specification but also with the business requirements graph — does this code solve the underlying problem?

• Qwen3 Coder Next — new generation for development. Transitioning to a new generation of coders always carries a risk of regressions — what the previous version did well, the new one may do worse. SymFSM when switching models automatically runs a set of benchmark tasks through both old and new versions and compares graph structures — regression becomes visible immediately, not after user complaints.

• Qwen3 Coder 30B A3B Instruct — MoE for code. Instructing a MoE model requires particular precision — it’s unclear which expert will process the instruction and how it will interpret it. SymFSM translates the instruction into a formal requirements graph, which is unambiguously interpreted regardless of which expert is activated.

• Qwen3 Max — top-tier model for complex tasks. Top-tier models are prone to the “expert effect” — they give a confident answer even when it would be honest to say “insufficient data.” SymFSM checks whether all nodes in the answer graph have justification in the source data — and if not, the repair mechanism requests clarification instead of inventing it.

• Qwen3 Max Thinking — with reasoning mode. Thinking mode encourages the model to over-reason — it goes through chains that don’t lead to the goal, wasting time and tokens. SymFSM before activating thinking mode builds the optimal trajectory and passes the already filtered path to the model.

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🔷 Mistral Models — European Solutions for Code and Text

• Mistral Small 2603 — compact and fast. Ideal for embedding in microservices where every call must take milliseconds and cost fractions of a cent. Problem: in microservice architecture, one service’s error cascades and destroys the entire pipeline. SymFSM checks the model’s output for contract compliance (structure, types, coherence) before passing to the next service.

• Mistral Large 2512 — flagship for complex tasks and code. European model with a focus on privacy-sensitive industries: finance, healthcare, government. In these domains, explainability is as important as accuracy. SymFSM reveals the full reasoning trajectory for audit — why the model reached a particular conclusion is visible on the graph.

• Mistral Small 3.2 24B Instruct — open model for local deployment. When deployed in isolated (air-gapped) environments, the model has no external knowledge access — and starts “making up” facts. SymFSM strictly restricts the answer graph to only those entities confirmed in loaded documents.

• Mistral Medium 3.1 — balanced model. Balance means the absence of distinct advantages — the model performs moderately across the board. SymFSM, knowing the model’s profile, for each specific task chooses a cognitive strategy that maximizes the model’s strengths and works around its weaknesses.

• Codestral 2508 — specialized for programming. Narrow specialization creates blind spots — the model writes excellent code but doesn’t understand the business context in which that code will operate. SymFSM adds business nodes to the task graph and checks that the generated code is reachable not only technically but also within business constraints.

• Mistral Nemo — lightweight open model. Attractive to researchers and students due to low hardware requirements. Problem: academic experiments often require reproducibility, but LLMs are non-deterministic. SymFSM captures the reasoning structure, and even if the model’s text varies from run to run, the logical trajectory remains verifiable.

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🔷 Moonshot (Kimi) Models — Universal Solutions with Focus on Reasoning

• Kimi K2.7 Code — for programming. Generates new code well, but poorly analyzes existing code — doesn’t see implicit dependencies and side effects. SymFSM builds a call graph and dependency graph from existing code using automata, and the model receives a structured representation it can work with more accurately.

• Kimi K2.6 — universal model. Universality provokes suboptimal strategy selection — the model solves engineering problems the same way it solves textual ones. SymFSM identifies the task domain (engineering, analysis, creative) and forcibly directs thinking along the corresponding trajectory.

• Kimi K2.5 — with a balance of speed and quality. Under streaming load, the balance often shifts toward speed — and quality drops unnoticed by the operator. SymFSM tracks quality metrics in real-time: if the share of responses with broken coherence starts rising, the system signals the need to reduce load or switch models.

• Kimi K2 Thinking — for complex reasoning. Thinking mode tends toward “over-reflection” — the model loops, re-verifying already proven facts. SymFSM recognizes cycles in the reasoning graph and breaks them, marking proven nodes and preventing the model from re-justifying them.

• Kimi K2-0905 — version from September 2025. Using dated versions in long-lived projects is dangerous — the model may be discontinued by the provider. SymFSM maintains a registry of active and deprecated models and warns in advance about the need to migrate to the current version. Deprecated model — consider upgrading to Kimi K2.7 Code.

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🔷 Z AI (GLM) Models — Flagship and Multimodal Solutions

• GLM-5.2 — flagship with 1M context. Large context and flagship ambitions create the expectation that the model will handle any task “out of the box.” SymFSM prevents reliance on “maybe” — even with a flagship model, every response undergoes formal verification — the model’s status does not exempt it from checking.

• GLM-5.1 — newest model for agents and code. Newness means a lack of operational experience — the community hasn’t yet accumulated a base of known issues and workarounds. SymFSM compensates for this: it treats the new model with an elevated level of checks, gradually lowering it as successful cognitive trajectories accumulate.

• GLM-5 Turbo — accelerated version. Acceleration always has a cost — for Turbo versions, this typically means reduced reasoning depth on complex tasks. SymFSM measures query complexity by the number of graph nodes and, for complex tasks, adds additional verification cycles, compensating for the model’s shortened internal reasoning.

• GLM-5 — powerful model for complex tasks. Powerful models tend to “get lost in details,” losing sight of the overall goal. SymFSM maintains focus: the graph always has a root node — the task’s goal — and each new node is checked for whether it brings the system closer to that goal.

• GLM-4.7 Flash — fast and affordable. The combination of price and speed makes this model a candidate for high-load systems where manual moderation of responses is impossible. SymFSM becomes an automatic moderator of logical quality: the response either passes verification and reaches the user, or is sent for repair.

• GLM-4.7 — strong universal model. In corporate installations, the model often works with internal documents that contain outdated or contradictory data. SymFSM identifies contradictions in the corporate knowledge base before they become the basis for management decisions.

• GLM-4.6V — multimodal with image understanding. Analyzing technical drawings and schematics is particularly sensitive to “hallucinations” — the model may “see” a non-existent element in a drawing. SymFSM in Anti-Fantasy mode requires that every element added to the graph from visual analysis has confirmation in image coordinates.

• GLM-4.6 — proven model for code and dialogues. Proven track record creates a risk of automatic trust — “GLM-4.6 always answered these questions correctly.” SymFSM does not believe in statistics — every response is verified here and now, regardless of past success history.

• GLM-4.5V — multimodal from previous generation. Using previous-generation multimodal models is risky: image recognition quality may have significantly improved in newer versions, and you don’t notice. SymFSM when using previous-generation models by default activates a “visual input enhanced control” flag. Deprecated — GLM-4.6V is available.

• GLM-4.5 — reliable open model. Openness allows auditing the model itself, but not its outputs on a specific task. SymFSM makes specific outputs verifiable: anyone can reproduce the path through the graph and verify the correctness of each step.

• GLM-4.5 Air — lightweight version for fast responses. In chat applications, a fast but logically contradictory response destroys user trust faster than a slow but correct one. SymFSM balances: on simple queries, passes the response without delay; on complex ones, holds it for verification, informing the user of the reason.

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🔷 Xiaomi (MiMo) Models — Universal Solutions with 1M Context

• MiMo V2.5 Pro — with 1M context. Large context and Pro status mean the model is positioned for corporate use, where the cost of errors is high and explainability is mandatory. SymFSM provides both: formal verification to reduce errors and graph reasoning for audit.

• MiMo V2.5 — for universal tasks. The model’s universality means it’s not optimized for any specific task class — and SymFSM takes optimization upon itself, choosing the cognitive strategy rather than relying on the model’s universal approach.

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🔷 MiniMax Models — Wide Lineup with 1M Context

• MiniMax M3 — with 1M context. A model with large context but no structural analysis mechanism is like a library without a catalog — data exists, but finding what you need without confusion is up to the user. SymFSM builds the catalog — a graph of entities and relationships — and works with it rather than raw context.

• MiniMax M2.7 — for text and code. Combining text and code in one model means it can generate documentation that doesn’t match the code, or code that doesn’t match the documentation. SymFSM cross-references the documentation graph and code graph and identifies discrepancies before publication.

• MiniMax M2.5 — with context caching. Caching speeds up repeated queries but creates a risk of using outdated context — data changed, but the model responds from cache. SymFSM at each request checks the hash of the context graph against the previous one and invalidates the cache on mismatch.

• MiniMax M2 Her — for personalized dialogues. Personalization based on dialogue history works well until the model starts “remembering” what the user didn’t say — adding phantom traits to the profile. SymFSM strictly separates confirmed facts about the user and the model’s assumptions in the graph, not allowing assumptions to become the basis for decisions.

• MiniMax M2.1 — improved version of M2. Versioning with “minor numbers” (M2 → M2.1) is often perceived as a minor update, but even a minor release can change behavior on specific queries. SymFSM after each model update runs a regression set of cognitive maps and compares trajectories before and after.

• MiniMax M2 — for universal tasks. The base version of the lineup, often used as the “entry point” for new users. Newbies tend to overestimate the capabilities of the base model, sending it queries that require Pro or Max versions. SymFSM evaluates the query and, if there’s a mismatch, recommends switching to a more suitable model, explaining why.

• MiniMax M1 — with 1M context. Previous generation with large context — the model may have had architectural limitations fixed in M2/M3, but is attractive for its price. SymFSM for previous-generation models automatically activates a “high-control” mode and does not allow it to be used in accuracy-critical scenarios.

• MiniMax-01 — with very large context. Extremely large context encourages extremely long queries, in which even a large model drowns. SymFSM forcibly segments ultra-long queries into semantic blocks, builds a graph between blocks, and passes structured material to the model.

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🔷 Sber (GigaChat) Models — Solutions for Russian-Language Tasks

• GigaChat 2 — for Russian-language tasks. Specialization in Russian gives an advantage in understanding local context, but doesn’t protect against logical errors common to all LLMs. SymFSM works with GigaChat 2 like any other model: builds a graph, checks reachability, triggers repair on breaks.

• GigaChat 2 Pro — advanced model. Pro status in the Russian-language segment often means better understanding of legal and bureaucratic texts — areas where a logical error can have legal consequences. SymFSM particularly thoroughly verifies outputs in the legal domain: every claim must be grounded in a specific node in the regulatory document graph.

• GigaChat 2 Max — top-tier model. The top Russian-language model means maximum token cost among Russian-language solutions. SymFSM optimizes costs: pre-flight query checking ensures that expensive tokens are not wasted on an obviously unreachable or incorrectly formulated task.

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🔧 Reference Table: Model Selection by Provider and Task Type

This table groups all available models by provider and task type. Models marked as deprecated should be replaced with more current versions to avoid quality degradation.

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⚙️ What Happens “Under the Hood” with Deprecated Models

When connecting models from the “deprecated” category, SymFSM automatically activates additional verification contours. This means that for Kimi K2-0905, GLM-4.5V, as well as GPT-3.5 Turbo, Claude 3.5 Haiku, and other marked models, the system performs 30–50 % more validation operations than for current versions. This approach allows safe use of older models where cost savings are important, without sacrificing logical accuracy. SymFSM compensates for model age with enhanced controls — ensuring stable quality across the entire LLM fleet, from the newest flagships to time-tested solutions.

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⏱️ Runtime Estimation: How Fast Will a Model Handle a Batch of Tasks

When selecting a model for production use, speed is as critical as quality. SymFSM accounts for each model’s token generation speed and estimates the approximate time for typical tasks, helping you choose the optimal balance between speed, cost, and quality.

Below are approximate figures for a batch of 100 queries (each requiring ~1,000 output tokens after SymFSM cognitive processing).

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Important Note on API Load

The figures in the table above reflect the pure generation time of a single model. However, SymFSM does not send just one request — to build a correct and verifiable reasoning chain, the system performs between 5 and 15 iterative requests to the model per single task. The exact number of requests depends on the complexity of the cognitive graph and how the thinking process unfolds: the more complex the task and the more clarifications and checks are required, the closer the number of requests gets to the upper bound (15).

Consequently, the actual task completion time can be 5–15 times higher than the single-request time shown in the table. This is because SymFSM sequentially builds, verifies, refines, and validates each reasoning step before delivering the final answer. This approach dramatically reduces the error rate and guarantees logical consistency of the result — which is critical for business tasks where the cost of mistakes is high.

Recommendation: when planning your budget and execution time, take into account not only the model’s generation speed but also the number of iterative calls SymFSM performs to achieve guaranteed quality.

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