Evals#
gptme provides LLMs with a wide variety of tools, but how well do models make use of them? Which tasks can they complete, and which ones do they struggle with? How far can they get on their own, without any human intervention?
To answer these questions, we have created an evaluation suite that tests the capabilities of LLMs on a wide variety of tasks.
Note
The evaluation suite is still tiny and under development, but the eval harness is fully functional.
Recommended Model#
The recommended model is Claude Sonnet 4.5 (anthropic/claude-sonnet-4-5 and openrouter/anthropic/claude-sonnet-4-5) for its:
Strong agentic capabilities
Strong coder capabilities
Strong performance across all tool types and formats
Reasoning capabilities
Vision & computer use capabilities
Decent alternatives include:
Gemini 3 Pro (
openrouter/google/gemini-3-pro-preview,gemini/gemini-3-pro-preview)GPT-5, GPT-4o (
openai/gpt-5,openai/gpt-4o)Grok 4 (
xai/grok-4,openrouter/x-ai/grok-4)Qwen3 Coder 480B A35B (
openrouter/qwen/qwen3-coder)Kimi K2 (
openrouter/moondreamai/kimi-k2-thinking,openrouter/moondreamai/kimi-k2)MiniMax M2 (
openrouter/minimax/minimax-m2)Llama 3.1 405B (
openrouter/meta-llama/llama-3.1-405b-instruct)DeepSeek V3 (
deepseek/deepseek-chat)DeepSeek R1 (
deepseek/deepseek-reasoner)
Note that some models may perform better or worse with different --tool-format options (markdown, xml, or tool for native tool-calling).
Note that many providers on OpenRouter have poor performance and reliability, so be sure to test your chosen model/provider combination before committing to it. This is especially true for open weight models which any provider can host at any quality. You can choose a specific provider by appending with :provider, e.g. openrouter/qwen/qwen3-coder:alibaba/opensource.
Note that pricing for models varies widely when accounting for caching, making some providers much cheaper than others. Anthropic is known and tested to cache well, significantly reducing costs for conversations with many turns.
You can get an overview of actual model usage in the wild from the OpenRouter app analytics for gptme.
Usage#
You can run the simple hello eval like this:
gptme-eval hello --model anthropic/claude-sonnet-4-5
However, we recommend running it in Docker to improve isolation and reproducibility:
make build-docker
docker run \
-e "ANTHROPIC_API_KEY=<your api key>" \
-v $(pwd)/eval_results:/app/eval_results \
gptme-eval hello --model anthropic/claude-sonnet-4-5
Available Evals#
The current evaluations test basic tool use in gptme, such as the ability to: read, write, patch files; run code in ipython, commands in the shell; use git and create new projects with npm and cargo. It also has basic tests for web browsing and data extraction.
Results#
Here are the results of the evals we have run so far:
$ gptme-eval eval_results/*/eval_results.csv
Model hello hello-patch hello-ask prime100 init-git init-rust whois-superuserlabs-ceo
--------------------------------------------- ---------------- ---------------- ---------------- ---------------- ---------------- ------------- -------------------------
anthropic/claude-3-5-haiku-20241022 ✅ 55/57 473tk ✅ 56/57 386tk ✅ 56/57 468tk ✅ 56/57 912tk ✅ 54/56 922tk ❓ N/A ✅ 1/1 2619tk
anthropic/claude-3-5-haiku-20241022@markdown ✅ 11/12 373tk ✅ 12/12 332tk ✅ 12/12 407tk ✅ 11/12 758tk ✅ 11/12 830tk ❓ N/A ❓ N/A
anthropic/claude-3-5-haiku-20241022@tool 🔶 67/126 287tk 🔶 68/126 330tk 🔶 68/126 433tk ❌ 15/126 595tk 🔶 73/126 643tk ❓ N/A ❓ N/A
anthropic/claude-3-5-haiku-20241022@xml 🔶 67/114 298tk 🔶 68/114 329tk 🔶 46/114 435tk ❌ 11/114 715tk 🔶 62/114 638tk ❓ N/A ❓ N/A
anthropic/claude-3-5-sonnet-20240620 ✅ 34/34 630tk ✅ 34/34 542tk ✅ 34/34 710tk ✅ 34/34 924tk ✅ 34/34 1098tk ✅ 4/4 1504tk 🔶 3/4 1306tk
anthropic/claude-3-5-sonnet-20241022 ✅ 54/56 378tk ✅ 54/56 371tk ✅ 54/56 416tk ✅ 55/56 869tk ✅ 55/56 754tk ❓ N/A ❓ N/A
anthropic/claude-3-5-sonnet-20241022@markdown 🔶 92/175 292tk 🔶 132/175 317tk 🔶 136/175 373tk 🔶 136/175 603tk 🔶 136/175 591tk ❓ N/A ❓ N/A
anthropic/claude-3-5-sonnet-20241022@tool 🔶 80/175 247tk 🔶 59/175 320tk 🔶 80/175 366tk 🔶 77/175 501tk 🔶 70/175 539tk ❓ N/A ❓ N/A
anthropic/claude-3-5-sonnet-20241022@xml ❌ 10/163 276tk 🔶 80/163 298tk 🔶 76/163 347tk ❌ 12/163 544tk 🔶 41/163 574tk ❓ N/A ❓ N/A
anthropic/claude-3-haiku-20240307 ✅ 34/34 388tk ✅ 34/34 375tk ✅ 34/34 432tk ❌ 6/34 781tk 🔶 24/34 903tk 🔶 3/4 670tk ✅ 4/4 1535tk
anthropic/claude-haiku-4-5@tool ✅ 49/49 342tk ✅ 49/49 456tk ✅ 49/49 527tk ❌ 2/49 919tk ✅ 44/49 963tk ❓ N/A ❓ N/A
anthropic/claude-haiku-4-5@xml ✅ 48/49 384tk ✅ 49/49 476tk ✅ 49/49 557tk ✅ 49/49 835tk 🔶 38/49 923tk ❓ N/A ❓ N/A
anthropic/claude-opus-4-1-20250805@markdown 🔶 1/2 393tk ❌ 0/1 197tk ❌ 0/1 204tk ❌ 0/1 190tk ❌ 0/1 186tk ❓ N/A ❓ N/A
anthropic/claude-opus-4-1-20250805@tool ❌ 0/1 181tk ❌ 0/1 196tk ❌ 0/1 202tk ❌ 0/1 188tk ❌ 0/1 184tk ❓ N/A ❓ N/A
anthropic/claude-opus-4-1-20250805@xml 🔶 1/2 372tk ❌ 0/1 197tk ❌ 0/1 205tk ❌ 0/1 191tk ❌ 0/1 187tk ❓ N/A ❓ N/A
anthropic/claude-sonnet-4-20250514@markdown ✅ 1/1 439tk ✅ 1/1 480tk ✅ 1/1 532tk ✅ 1/1 1180tk ✅ 1/1 2006tk ❓ N/A ❓ N/A
anthropic/claude-sonnet-4-20250514@tool ✅ 1/1 283tk ✅ 1/1 320tk ✅ 1/1 471tk ✅ 1/1 1133tk ✅ 1/1 1070tk ❓ N/A ❓ N/A
anthropic/claude-sonnet-4-20250514@xml ✅ 1/1 404tk ✅ 1/1 495tk ✅ 1/1 584tk ✅ 1/1 1223tk ❌ 0/1 1422tk ❓ N/A ❓ N/A
deepseek/deepseek-chat@markdown ✅ 1/1 429tk ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A
deepseek/deepseek-reasoner@markdown ✅ 1/1 742tk ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A
deepseek/deepseek-reasoner@xml ✅ 1/1 680tk ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A ❓ N/A
gemini/gemini-1.5-flash-latest ❌ 0/42 28tk ❌ 0/42 28tk ❌ 0/42 28tk ❌ 0/42 28tk ❌ 0/42 28tk ❓ N/A ❓ N/A
gemini/gemini-1.5-flash-latest@markdown ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❓ N/A ❓ N/A
gemini/gemini-1.5-flash-latest@tool ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❌ 0/12 29tk ❓ N/A ❓ N/A
gemini/gemini-2.5-flash@markdown ✅ 1/1 419tk ✅ 1/1 450tk ✅ 1/1 516tk ✅ 1/1 735tk ❓ N/A ❓ N/A ❓ N/A
gemini/gemini-2.5-flash@xml ✅ 1/1 432tk ✅ 1/1 468tk ✅ 1/1 534tk ✅ 1/1 836tk ❓ N/A ❓ N/A ❓ N/A
groq/moonshotai/kimi-k2-instruct@markdown ✅ 1/1 407tk ✅ 1/1 497tk ✅ 1/1 575tk ✅ 1/1 1079tk ❓ N/A ❓ N/A ❓ N/A
groq/moonshotai/kimi-k2-instruct@xml ✅ 1/1 424tk ✅ 1/1 498tk ✅ 1/1 587tk ✅ 1/1 1150tk ❓ N/A ❓ N/A ❓ N/A
groq/qwen/qwen3-32b@markdown ✅ 1/1 709tk ✅ 1/1 711tk ❌ 0/1 0tk ❌ 0/1 0tk ✅ 1/1 2609tk ❓ N/A ❓ N/A
groq/qwen/qwen3-32b@tool ❌ 0/1 172tk ❌ 0/1 187tk ❌ 0/1 194tk ❌ 0/1 179tk ❌ 0/1 175tk ❓ N/A ❓ N/A
groq/qwen/qwen3-32b@xml ✅ 1/1 1918tk ✅ 1/1 725tk ✅ 1/1 1115tk ❌ 0/1 5171tk ❌ 0/1 4367tk ❓ N/A ❓ N/A
openai/gpt-4-turbo ✅ 3/3 255tk ✅ 3/3 312tk ✅ 3/3 376tk ✅ 3/3 527tk ✅ 4/4 590tk ✅ 4/4 784tk ✅ 6/7 819tk
openai/gpt-4o ✅ 90/91 325tk ✅ 90/91 313tk 🔶 65/91 337tk 🔶 35/91 441tk 🔶 67/91 626tk ✅ 5/5 663tk ✅ 5/5 1253tk
openai/gpt-4o-mini 🔶 60/92 269tk ✅ 91/92 321tk ✅ 91/92 377tk 🔶 62/92 510tk ✅ 80/92 759tk ✅ 6/6 813tk ✅ 6/6 951tk
openai/gpt-4o-mini@markdown ❌ 1/12 217tk ✅ 12/12 302tk ✅ 12/12 361tk 🔶 3/12 440tk ✅ 12/12 823tk ❓ N/A ❓ N/A
openai/gpt-4o-mini@tool ✅ 166/175 283tk ✅ 158/175 319tk ✅ 175/175 408tk 🔶 137/175 597tk ✅ 175/175 678tk ❓ N/A ❓ N/A
openai/gpt-4o@markdown 🔶 72/175 286tk 🔶 83/175 361tk ✅ 158/175 380tk ❌ 23/175 533tk 🔶 47/175 584tk ❓ N/A ❓ N/A
openai/gpt-4o@tool 🔶 43/175 285tk 🔶 120/175 428tk 🔶 124/175 435tk 🔶 75/175 706tk ✅ 173/175 817tk ❓ N/A ❓ N/A
openai/gpt-4o@xml ❌ 8/163 240tk 🔶 41/163 280tk ❌ 5/163 260tk ❌ 1/163 388tk ❌ 2/163 298tk ❓ N/A ❓ N/A
openai/gpt-5-mini@markdown ✅ 1/1 379tk ✅ 1/1 358tk ✅ 1/1 884tk ❌ 0/1 0tk ❌ 0/1 715tk ❓ N/A ❓ N/A
openai/gpt-5-mini@tool ✅ 1/1 286tk ✅ 1/1 294tk ✅ 1/1 1150tk ❌ 0/1 0tk ❌ 0/1 0tk ❓ N/A ❓ N/A
openai/gpt-5-mini@xml ❌ 0/1 331tk ✅ 1/1 386tk ✅ 1/1 922tk ✅ 1/1 1369tk ✅ 1/1 1614tk ❓ N/A ❓ N/A
openai/gpt-5@markdown ✅ 1/1 228tk ✅ 1/1 273tk ✅ 1/1 367tk ✅ 1/1 695tk ❌ 0/1 0tk ❓ N/A ❓ N/A
openai/gpt-5@tool ✅ 1/1 306tk ✅ 1/1 299tk ✅ 1/1 348tk ❌ 0/1 0tk ❌ 0/1 0tk ❓ N/A ❓ N/A
openai/gpt-5@xml ✅ 1/1 237tk ❌ 0/1 218tk ❌ 0/1 217tk ❌ 0/1 678tk ❌ 0/1 255tk ❓ N/A ❓ N/A
openai/o1-mini ✅ 3/3 354tk ✅ 3/3 431tk ✅ 3/3 460tk 🔶 2/3 567tk 🔶 3/5 2222tk 🔶 1/5 1412tk 🔶 2/3 813tk
openai/o1-preview ✅ 2/2 308tk ✅ 2/2 570tk 🔶 1/2 549tk ✅ 2/2 490tk ✅ 3/3 823tk ✅ 1/1 656tk ✅ 1/1 1998tk
google/gemini-flash-1.5 ✅ 2/2 225tk ✅ 2/2 401tk ✅ 2/2 430tk ❌ 0/2 296tk ✅ 1/1 686tk ❌ 0/1 661tk ✅ 1/1 1014tk
google/gemini-pro-1.5 ✅ 1/1 341tk ✅ 1/1 419tk ✅ 1/1 456tk ✅ 1/1 676tk 🔶 2/3 431tk 🔶 1/2 1016tk ✅ 2/2 1308tk
google/gemma-2-27b-it ✅ 1/1 288tk ✅ 1/1 384tk ✅ 1/1 446tk ✅ 1/1 714tk ✅ 1/1 570tk ❌ 0/1 535tk ❌ 0/1 235tk
google/gemma-2-9b-it ❌ 0/2 186tk ✅ 2/2 370tk ✅ 2/2 368tk ❌ 0/2 545tk ✅ 1/1 492tk ❌ 0/1 1730tk ❌ 0/1 352tk
meta-llama/llama-3.1-405b-instruct 🔶 69/91 244tk ✅ 74/91 417tk ✅ 73/91 364tk 🔶 71/91 440tk 🔶 60/91 488tk 🔶 2/5 255tk ❌ 0/5 85tk
meta-llama/llama-3.1-405b-instruct@markdown ✅ 11/12 318tk ✅ 10/12 269tk ✅ 10/12 366tk 🔶 8/12 516tk ✅ 11/12 600tk ❓ N/A ❓ N/A
meta-llama/llama-3.1-405b-instruct@tool ❌ 0/12 184tk ❌ 0/12 198tk ❌ 0/12 214tk ❌ 0/12 190tk ❌ 0/12 189tk ❓ N/A ❓ N/A
meta-llama/llama-3.1-70b-instruct ✅ 5/6 367tk ✅ 5/6 424tk ✅ 6/6 452tk 🔶 2/6 546tk ✅ 5/6 813tk 🔶 3/4 682tk 🔶 2/3 1461tk
meta-llama/llama-3.1-70b-instruct@xml 🔶 115/163 262tk ❌ 8/163 358tk 🔶 33/163 436tk ❌ 13/163 384tk 🔶 85/163 454tk ❓ N/A ❓ N/A
meta-llama/llama-3.1-8b-instruct ✅ 1/1 277tk ✅ 1/1 441tk ❌ 0/1 400tk ❌ 0/1 5095tk ✅ 1/1 2266tk ❓ N/A ❓ N/A
meta-llama/llama-3.2-11b-vision-instruct ✅ 2/2 352tk ✅ 2/2 493tk ❌ 0/2 479tk ✅ 2/2 2643tk ❓ N/A ❓ N/A ❓ N/A
meta-llama/llama-3.2-90b-vision-instruct 🔶 2/4 237tk 🔶 2/4 288tk 🔶 3/4 336tk 🔶 1/4 233tk ❓ N/A ❓ N/A ❓ N/A
mistralai/magistral-medium-2506@markdown ✅ 1/1 531tk ✅ 1/1 569tk ✅ 1/1 666tk ✅ 1/1 1106tk ❓ N/A ❓ N/A ❓ N/A
mistralai/magistral-medium-2506@tool ❌ 0/1 465tk ❌ 0/1 604tk ❌ 0/1 0tk ❌ 0/1 0tk ❓ N/A ❓ N/A ❓ N/A
mistralai/magistral-medium-2506@xml ✅ 1/1 516tk ✅ 1/1 568tk ❌ 0/1 552tk ✅ 1/1 1075tk ❓ N/A ❓ N/A ❓ N/A
moonshotai/kimi-k2-0905@markdown ✅ 2/2 464tk ✅ 2/2 590tk ✅ 2/2 613tk 🔶 1/2 650tk ❓ N/A ❓ N/A ❓ N/A
moonshotai/kimi-k2-0905@tool ❌ 0/1 397tk ❌ 0/1 483tk ❌ 0/1 592tk ✅ 1/1 990tk ❓ N/A ❓ N/A ❓ N/A
moonshotai/kimi-k2-0905@xml ✅ 1/1 441tk ✅ 1/1 563tk ✅ 1/1 848tk ❌ 0/1 598tk ❓ N/A ❓ N/A ❓ N/A
nousresearch/hermes-2-pro-llama-3-8b ✅ 1/1 341tk ❌ 0/1 4274tk ❌ 0/1 3760tk ❌ 0/1 659tk ❓ N/A ❓ N/A ❓ N/A
nousresearch/hermes-3-llama-3.1-405b ✅ 2/2 317tk ✅ 2/2 420tk ✅ 2/2 325tk ✅ 2/2 410tk ✅ 1/1 821tk ✅ 1/1 758tk ✅ 1/1 1039tk
nousresearch/hermes-3-llama-3.1-70b ❌ 0/2 173tk ❌ 0/2 187tk ❌ 0/2 202tk ❌ 0/2 177tk ❓ N/A ❓ N/A ❓ N/A
nousresearch/hermes-4-70b@markdown ❌ 0/1 439tk ✅ 1/1 612tk ✅ 1/1 1235tk ❌ 0/1 0tk ❓ N/A ❓ N/A ❓ N/A
nousresearch/hermes-4-70b@tool ✅ 1/1 476tk ✅ 1/1 536tk ❌ 0/1 1310tk ❌ 0/1 0tk ❓ N/A ❓ N/A ❓ N/A
nousresearch/hermes-4-70b@xml ✅ 1/1 466tk ✅ 1/1 516tk ✅ 1/1 631tk ❌ 0/1 1255tk ❓ N/A ❓ N/A ❓ N/A
qwen/qwen3-max@markdown ✅ 1/1 422tk ✅ 1/1 492tk 🔶 1/2 615tk ✅ 1/1 949tk ❓ N/A ❓ N/A ❓ N/A
qwen/qwen3-max@tool ✅ 1/1 436tk ✅ 1/1 519tk ✅ 1/1 546tk ✅ 1/1 663tk ❓ N/A ❓ N/A ❓ N/A
qwen/qwen3-max@xml ✅ 1/1 437tk ✅ 1/1 530tk ✅ 1/1 580tk ✅ 1/1 901tk ❓ N/A ❓ N/A ❓ N/A
x-ai/grok-4-fast:free@markdown ✅ 1/1 561tk 🔶 1/2 760tk ✅ 1/1 1326tk ✅ 1/1 1016tk ❓ N/A ❓ N/A ❓ N/A
x-ai/grok-code-fast-1@markdown ✅ 1/1 661tk ❌ 0/1 1385tk ✅ 1/1 829tk ✅ 1/1 955tk ❓ N/A ❓ N/A ❓ N/A
x-ai/grok-code-fast-1@tool ✅ 1/1 663tk ❌ 0/1 2590tk ❌ 0/1 1415tk ✅ 1/1 1807tk ❓ N/A ❓ N/A ❓ N/A
x-ai/grok-code-fast-1@xml ❌ 0/1 485tk ❌ 0/1 1652tk ✅ 1/1 759tk ✅ 1/1 1112tk ❓ N/A ❓ N/A ❓ N/A
We are working on making the evals more robust, informative, and challenging.
Other evals#
We have considered running gptme on other evals such as SWE-Bench, but have not finished it (see PR #142).
If you are interested in running gptme on other evals, drop a comment in the issues!