Radar vs matrix
Use the radar when the question is task fit, not raw model shape
The matrix stays technical. The radar exists to answer which lane to start with for a specific operating scenario.
Choose the right model lane for the task before the stack hardens around the wrong default.
Model Fit Radar is the fast decision layer above the matrix. It answers which lane to start with for coding, long reasoning, multimodal work, local-open strategy, cheap routing and agent workers.
ℹ️
Snapshot rules
Model Fit Radar is the scenario layer. It uses a 2026-03 local snapshot grounded in the same official-source lane data as the AI matrix. Use it after the vendor question is narrow enough and before you need a practical operating recipe. If local-open fit now depends on serving limits, jump to Inference Hardware Guide.
Scenarios covered
6
Decision-first model picks
Premium-first lanes
3
Frontier or final-pass fits
Low-cost lanes
1
Routing or worker-friendly
Local-open lanes
1
Self-host or open-weight strategy
Escalation
Do not force one model to cover every lane
A cheap worker plus a stronger final pass usually beats one universal lane on both cost and control.
Separation
Keep multimodal, local-open and coding lanes visibly distinct
The wrong default usually appears when all model choices are flattened into one vague "best model" list.
Scenario-first model picks
| Scenario | Primary pick | Use when | Cost and latency | Avoid when |
|---|---|---|---|---|
| Coding GPT-5.4 fits best when the work combines code, tools and long agent loops against a real repo. | GPT-5.4 Runner-up: Claude Sonnet 4 If the flow is pure coding and cost-sensitive, keep a specialist fallback or a mini lane available. | Use it for bounded refactors, large PRs, tool-guided debugging and tasks where the final pass must stay strong. | Premium Moderate | It is not the best first choice for cheap autocomplete, bulk triage or simple workers. Official source: OpenAI GPT-5.4 |
| Long reasoning Best first option when long context, planning and continuity across tools matter. | GPT-5.4 Runner-up: Claude Sonnet 4 If reasoning sits inside a larger workflow, reserve this lane for the planner or final pass. | Use it for long-document analysis, technical decisions, demanding research and plans with multiple constraints. | Mid-high Moderate | It is not worth it for cheap classification, short drafts or jobs where throughput dominates. Official source: OpenAI GPT-5.4 |
| Cost and throughput Best fit when the goal is lowering cost per task without giving up modality or context entirely. | Gemini 2.5 Flash-Lite Runner-up: DeepSeek V3.2 The best operational reading is to use it as a cheap worker before escalating to a stronger lane. | Use it for routing, classification, drafts, ranking, filters and high-volume first passes. | Low Low | It should not carry the final decision on delicate reasoning, governance or difficult debugging. Official source: Gemini pricing |
| Multimodal Best option when audio, video, image and long documents enter the same decision flow. | Gemini 2.5 Pro Runner-up: GPT-5.4 mini If the use case collapses to text-only, drop to a cheaper or more specialized lane. | Use it for serious multimodal analysis, heavy documents, visual assets and mixed inputs. | Mid-high Moderate | It is not the first choice for cheap text-only work or tasks where multimodality adds little value. Official source: Gemini model docs |
| Local and open It is the most useful lane when the architecture needs self-host, private cloud or a real open-weight strategy. | Mistral Large 3 Runner-up: Ministral 3 8B When footprint matters more than maximum quality, drop to the edge-sized Ministral lane. | Use it for privacy, regional residence, private routers and stacks where hosting is part of the product. | Competitive Moderate | It is not the best bet if the team depends on the largest ecosystem or absolute frontier coding performance. Official source: Mistral model overview |
| Agents and subagents Best lane when you need cheap, fast and competent workers before escalating to a stronger approver. | GPT-5.4 mini Runner-up: GPT-5.4 The right operational split is mini for workers and GPT-5.4 for the planner, reviewer or final close. | Use it for subagents, decomposition, tool loops, browser workers and repeatable tasks with spend control. | Medium Medium-low | Do not leave the final answer entirely to it when the work demands sustained frontier reasoning. Official source: OpenAI GPT-5.4 mini |
Coding
GPT-5.4
Why: GPT-5.4 fits best when the work combines code, tools and long agent loops against a real repo.
Use when: Use it for bounded refactors, large PRs, tool-guided debugging and tasks where the final pass must stay strong.
Runner-up: Claude Sonnet 4
Ops note: If the flow is pure coding and cost-sensitive, keep a specialist fallback or a mini lane available.
Avoid when: It is not the best first choice for cheap autocomplete, bulk triage or simple workers.
Premium Moderate
Official source Long reasoning
GPT-5.4
Why: Best first option when long context, planning and continuity across tools matter.
Use when: Use it for long-document analysis, technical decisions, demanding research and plans with multiple constraints.
Runner-up: Claude Sonnet 4
Ops note: If reasoning sits inside a larger workflow, reserve this lane for the planner or final pass.
Avoid when: It is not worth it for cheap classification, short drafts or jobs where throughput dominates.
Mid-high Moderate
Official source Cost and throughput
Gemini 2.5 Flash-Lite
Why: Best fit when the goal is lowering cost per task without giving up modality or context entirely.
Use when: Use it for routing, classification, drafts, ranking, filters and high-volume first passes.
Runner-up: DeepSeek V3.2
Ops note: The best operational reading is to use it as a cheap worker before escalating to a stronger lane.
Avoid when: It should not carry the final decision on delicate reasoning, governance or difficult debugging.
Low Low
Official source Multimodal
Gemini 2.5 Pro
Why: Best option when audio, video, image and long documents enter the same decision flow.
Use when: Use it for serious multimodal analysis, heavy documents, visual assets and mixed inputs.
Runner-up: GPT-5.4 mini
Ops note: If the use case collapses to text-only, drop to a cheaper or more specialized lane.
Avoid when: It is not the first choice for cheap text-only work or tasks where multimodality adds little value.
Mid-high Moderate
Official source Local and open
Mistral Large 3
Why: It is the most useful lane when the architecture needs self-host, private cloud or a real open-weight strategy.
Use when: Use it for privacy, regional residence, private routers and stacks where hosting is part of the product.
Runner-up: Ministral 3 8B
Ops note: When footprint matters more than maximum quality, drop to the edge-sized Ministral lane.
Avoid when: It is not the best bet if the team depends on the largest ecosystem or absolute frontier coding performance.
Competitive Moderate
Official source Agents and subagents
GPT-5.4 mini
Why: Best lane when you need cheap, fast and competent workers before escalating to a stronger approver.
Use when: Use it for subagents, decomposition, tool loops, browser workers and repeatable tasks with spend control.
Runner-up: GPT-5.4
Ops note: The right operational split is mini for workers and GPT-5.4 for the planner, reviewer or final close.
Avoid when: Do not leave the final answer entirely to it when the work demands sustained frontier reasoning.
Medium Medium-low
Official source LLM route
Start at the routing layer if you still need to decide between vendor, matrix or workflow.
Provider compare
Choose the provider lane before you lock the model lane.
LLM matrix
Return to the technical comparison when context, price or deployment details matter more than scenario fit.
Workflow recipes
Jump into practical operating recipes once the model lane is already chosen.
Inference guide
Open hardware guidance when local-open or multimodal choices now depend on VRAM, RAM or power shape.