Best AI Models for Tool Calling
Ranking of language models by tool-use reliability — function calling, API orchestration and structured output for agent pipelines.Updated August 2026. Based on independent benchmark data.
Why This Matters
Choosing the right model for tool calling is about more than raw benchmark scores. The models below are ranked using a weighted blend of category-specific benchmarks, arena Elo, and the intelligence index — but real-world performance depends on your context window, latency budget, and integration path.
For tool calling, we weighted the benchmarks that most strongly predict success in production: task accuracy first, then speed and cost efficiency. Leaders on this page consistently outperform on both standardized tests and real-world workloads.
If you operate under a strict budget, check the Best Value LLMs ranking. If latency matters more than raw quality, see Fastest LLMs. Both pages use the same underlying dataset with different weightings.
| # | Model | Provider | Score | Speed | Price (output) |
|---|---|---|---|---|---|
| 1 | Kimi K3 | Moonshot AI | 94.6 | 37 t/s | $15/M |
| 2 | Claude Fable 5 | Anthropic | 90.1 | 71 t/s | $50/M |
| 3 | GPT-6 Astra | OpenAI | 82.0 | 82 t/s | $50/M |
| 4 | Claude Opus 4.8 | Anthropic | 78.1 | 50 t/s | $15/M |
| 5 | Claude Opus 4.7 | Anthropic | 73.9 | 80 t/s | $15/M |
| 6 | Gemini 3.5 Flash | 72.1 | 267 t/s | $9/M | |
| 7 | GPT-5.5 | OpenAI | 68.8 | 60 t/s | $10/M |
| 8 | Qwen3.7 Max | Alibaba | 67.3 | 203 t/s | $7.5/M |
| 9 | Gemini 3.1 Pro | 67.0 | 136 t/s | $12/M | |
| 10 | GLM-5.2 | Z.ai | 66.4 | 164 t/s | $4.29/M |
| 11 | Qwen3.8 Flash Next | Alibaba | 64.5 | 168 t/s | $0.4/M |
| 12 | GLM-5.3 Flash | Z.ai | 61.6 | 182 t/s | $0.3/M |
| 13 | MiMo V2.5 Pro | Xiaomi | 57.3 | 65 t/s | $0.87/M |
| 14 | Granite 4.2 30B Instruct | IBM | 56.6 | 78 t/s | $1.2/M |
| 15 | GPT-5.6 Sol | OpenAI | 55.4 | 72 t/s | $30/M |
| 16 | Qwen3.7 Plus | Alibaba | 53.6 | 54 t/s | $1.6/M |
| 17 | Claude Opus 4.6 | Anthropic | 52.3 | 50 t/s | $15/M |
| 18 | DeepSeek V4.1 Flash | DeepSeek | 52.0 | 214 t/s | $1.2/M |
| 19 | DeepSeek V4 Pro | DeepSeek | 51.6 | 66 t/s | $0.87/M |
| 20 | Muse Spark | Meta | 50.1 | 80 t/s | $0/M |
Top 5 Tool Calling — Quick Comparison
Methodology
Rankings are based on independent benchmark evaluations. The tool calling score is computed from multiple standardized benchmarks and normalized to 0–100. Data is updated regularly from public benchmark results.
Related Rankings
Frequently Asked Questions
Which AI model is most reliable for function calling?
Models leading TAU-bench and BFCL function-calling benchmarks produce the most reliable structured tool invocations.