Gorilla APIBench
Llama 3.1-405B 35.3% leads since Jul 2024 Only in system cards Models write calls to machine learning APIs from Hugging Face, TorchHub and TensorHub. Scored by AST match.
The same Gorilla team later built BFCL.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Code A program or patch run against tests.
Llama 3.1-405B leads with 35.3% among models released by Jul 2024
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in Jul 2024, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 Llama 3.1-405B 35.3%
- 2 Llama 3.1-70B 29.7%
- 3 Llama 3.1-8B 8.20%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Llama 3.1-405B | 35.3% | Jul 2024 | Model card | |
| 2 | Llama 3.1-70B | 29.7% | Jul 2024 | Model card | |
| 3 | Llama 3.1-8B | 8.20% | Jul 2024 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.
6 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 23 Nov 2024 | Llama 3.1-8B technical report | 8.2 | API-Bench (Gorilla), zero-shot function calling accuracy, post-trained (Table 22) | Capabilities |
| 23 Nov 2024 | Llama 3.1-70B technical report | 29.7 | API-Bench (Gorilla), zero-shot function calling accuracy, post-trained (Table 22) | Capabilities |
| 23 Nov 2024 | Llama 3.1-405B technical report | 35.3 | API-Bench (Gorilla), zero-shot function calling accuracy, post-trained (Table 22) | Capabilities |
| 23 Jul 2024 | Llama 3.1-8B model card | 8.2 | Gorilla Benchmark API Bench, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
| 23 Jul 2024 | Llama 3.1-70B model card | 29.7 | Gorilla Benchmark API Bench, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
| 23 Jul 2024 | Llama 3.1-405B model card | 35.3 | Gorilla Benchmark API Bench, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
More in Tool use
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- BFCL V4 In use Models call functions and tools, including multi-turn agentic tasks with web search and memory. Checked against expected calls and outcomes.
Qwen3.7-Max 75% - τ³-bench Banking In use Agents act as banking customer-service reps over many turns with a simulated user, following policy. Pass^1 on the final database state. Qwen 3.8 Max 55.1%
- Claw-Eval In use 300 human-verified agent tasks (service orchestration, multimodal, multi-turn dialogue) audited via traces and snapshots. Rubric scores with Pass^3 consistency. Claude Sonnet 4.6 81.4%
- DeepSearchQA In use Questions that need multi-step search and synthesis. Final answers graded against references. Kimi K3 95%