Methodology
Candidates must pass the selected capability and context gates. The ranking then combines primary-source specifications, current catalog price minima, provider breadth, and published benchmark rank where available.
Model Selection
AI Model Selector
Rank AI models for a workload using task fit, context, budget, benchmark coverage, and provider availability.
Interactive Tool
Build a Scenario
WorkloadCapability gate applied before ranking.
Ranking priorityChanges weights, not the eligibility gate.
| Result | Value | How to read it |
|---|---|---|
| Eligible models | 127 | Models that pass the selected evidence and constraint gates. |
| Ranked shortlist | 20 | Top candidates shown below. |
| Priced candidates | 80 | Models with both input and output price minima. |
| Rank | Model | Fit score | Context | Input / 1M | Workload cost | Published benchmark rank | Providers |
|---|---|---|---|---|---|---|---|
| 1 | GPT-5.6 LunaOpenAI | 85.66 | 1.05M tokens | $0.20 / 1M | $44.00 | 4 | 2 |
| 2 | GPT-5.6 SolOpenAI | 84.37 | 1.05M tokens | $2.00 / 1M | $400.00 | 2 | 2 |
| 3 | GPT-5.6 TerraOpenAI | 81.61 | 1.05M tokens | $2.00 / 1M | $440.00 | 3 | 2 |
| 4 | Gemini 3.1 ProGoogle DeepMind | 76.95 | 1.05M tokens | $2.00 / 1M | $440.00 | 5 | 1 |
| 5 | Qwen3.8-MaxQwen | 75.82 | 1M tokens | $1.65 / 1M | $264.02 | 9 | 3 |
| 6 | Gemini 3.5 FlashGoogle DeepMind | 74.26 | 1.05M tokens | $1.50 / 1M | $330.00 | 8 | 1 |
| 7 | Gemini 3 FlashGoogle DeepMind | 72.94 | 1.05M tokens | $0.50 / 1M | $110.00 | 11 | 1 |
| 8 | Gemini 3.6 FlashGoogle DeepMind | 71.54 | 1.05M tokens | $0.75 / 1M | $150.00 | 12 | 1 |
| 9 | Gemini 2.5 ProGoogle DeepMind | 71.16 | 1.05M tokens | $1.25 / 1M | $325.00 | 10 | 1 |
| 10 | Claude Opus 5Anthropic | 70.68 | 1M tokens | $5.00 / 1M | $1,000.00 | 6 | 3 |
| 11 | Gemini 3.5 Flash-LiteGoogle DeepMind | 70.62 | 1.05M tokens | $0.30 / 1M | $80.00 | 14 | 1 |
| 12 | Claude Opus 4.8Anthropic | 68.28 | 1M tokens | $5.00 / 1M | $1,000.00 | 7 | 2 |
| 13 | Claude Sonnet 5Anthropic | 67.35 | 1M tokens | $2.00 / 1M | $400.00 | 15 | 3 |
| 14 | Kimi-K3Moonshot AI | 66.89 | 1.05M tokens | $2.85 / 1M | $570.00 | 13 | 2 |
| 15 | Claude Fable 5Anthropic | 66.77 | 1M tokens | $10.00 / 1M | $2,000.00 | 1 | 3 |
| 16 | Kimi-K2.6Moonshot AI | 66.34 | 262.14K tokens | $0.75 / 1M | $145.00 | 18 | 2 |
| 17 | MiniMax-M3MiniMax | 65.73 | 1.05M tokens | $0.28 / 1M | $50.00 | 20 | 2 |
| 18 | DeepSeek-V4-ProDeepSeek | 64.84 | 1.05M tokens | $0.66 / 1M | $96.88 | 22 | 3 |
| 19 | Grok 4.6xAI | 64.45 | 500K tokens | $2.00 / 1M | $320.00 | 16 | 1 |
| 20 | DeepSeek-V4-FlashDeepSeek | 64.1 | 1.05M tokens | $0.09 / 1M | $12.15 | 24 | 3 |
Fit scores are transparent screening aids, not universal quality claims. Unknown capability, context, or price fields are never filled by inference.
Inputs
What the Calculation Needs
| Input | How it is used |
|---|---|
| Workload | General, coding, reasoning, vision, or structured-output work. |
| Context requirement | The minimum input and output capacity the request needs. |
| Monthly volume | Input and output tokens used to compare estimated spend. |
| Priority | Balance documented performance, cost, or context capacity. |
How to Interpret the Result
Treat the first rows as a research queue. Open each model record to verify the exact provider endpoint, price scope, source date, and any unknown fields before production use.
Boundaries
What the Result Does Not Prove
- A low published price may belong to only one provider or tier.
- Benchmark coverage differs by model and missing results are not zero quality.
- The selector does not measure your prompt quality, reliability, or regional latency.
Catalog values retain their source and freshness on the linked model, provider, benchmark, or comparison page. Editable scenario assumptions are not Model Markets measurements.
Continue the Analysis
Related Tools
| Tool | Next question |
|---|---|
| Price vs Performance | Screen for models that combine useful published performance with acceptable token economics. |
| LLM Cost Calculator | Price one explicit token workload against an exact published provider endpoint. |
| Model Router | Split routine and difficult requests across eligible models without pretending one model is optimal for every call. |
Questions
Model Selector FAQs
What does the AI Model Selector calculate?+
Turn workload constraints into a short, inspectable model shortlist instead of a universal best-model claim. It returns a ranked shortlist with fit reasons, estimated workload cost, sourced limits, and direct model links.
Does the AI Model Selector use current model data?+
Where the calculation needs model facts, it uses the current Model Markets catalog snapshot updated 2026-09-02. User-entered assumptions remain clearly editable, and unsupported values stay unknown rather than being inferred.
What should I verify before using the AI Model Selector result?+
A low published price may belong to only one provider or tier. Benchmark coverage differs by model and missing results are not zero quality. The selector does not measure your prompt quality, reliability, or regional latency. Open the linked canonical records and primary sources before making a production or purchasing decision.