MCP Atlas
Multi-step workflows over real MCP servers. Scores tool-use competency on realistic tasks.
500 public tasks of 1,000. Rest held back. Labs use different judge models, so self-reported scores vary.
- Made by
- Scale AI
- Released
- January 2026
- Kind
- Apps and MCP servers
- Level
- University
- Scored
- Graded by a model
- Answers
- Agent task
- Tasks
- Public, plus a private test set
- Strongest model tested
- Claude Opus 5.5 #4 on the capability index
- Last in a launch post
- November 2026
- Last model added
- November 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
The Model Context Protocol (MCP) is emerging as a standard interface through which large language model (LLM) agents discover and invoke external tools. However, existing MCP evaluations fall short along three key axes: realistic multi-step workflows with cross-server orchestration, breadth across authentic MCP servers rather than mocks, and structured, reproducible claim-level scoring disentangled from agent verbosity or style. We introduce MCP-Atlas, a benchmark for measuring tool-use competency against production MCP servers. MCP-Atlas contains 1,000 natural-language tasks written and verified by human experts spanning 36 real MCP servers and 220 tools. Prompts do not specify servers, tools, or parameters, requiring agents to identify relevant tools among semantically plausible distractors and to compose multi-step, cross-server workflows. Each task is scored with a claim-level rubric, where final answers are scored against atomic factual claims grounded in tool outputs. This answer-centric scoring permits valid alternative tool-call trajectories to receive credit. We pair this with an 11-category diagnostic taxonomy that disentangles tool-call failures from cognitive failures in task understanding, synthesis, parsing, and stopping. Evaluating 20 frontier models from six providers under matched task-level conditions, we find pass rates up to 82.2% at a 0.75 claim coverage threshold and a clear three-tier performance structure. Automated diagnostics show that 63.3% of diagnosed failures are cognitive rather than tool-call related. Notably, several high-performing models fail after successful tool execution due to premature stopping or incorrect synthesis. We release the task schema, containerized harness, claim evaluator, and a 500-task public split, while reserving a 500-task private split to preserve leaderboard integrity. The code is at https://github.com/scaleapi/mcp-atlas.
Gemini 4 Argon leads with 90.7%
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best.
Gemini 4 Argon leads with 90.7%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 90.7
Gemini 4 Argon
- 88.1
Muse Spark 1.1
- 87.2 Claude Fable 5.1
- 85.8 Claude Opus 5
- 85.4 Claude Opus 5.5
- 85.4
Inkling 2 Small
- 84.5
Qwen3.8-2.4T-A95B
- 84.2 GLM-5.3
- 83.6
Gemini 3.5 Flash
- 83.3 Claude Fable 5
- 82.3 Kimi K3
- 82.2
Muse Spark
- 82.2 Claude Opus 4.8
- 81.8 GPT-5.6 Sol
- 79.2
Inkling-Small
- 79.1 Claude Opus 4.7
- 78.2
Gemini 3.1 Pro
- 77.8 GLM-5.2
- 76.8 Claude Opus 4.6
- 76.0
Inkling
- 75.6 GLM-5.1
- 75.3 GPT-5.5
- 70.6 GPT-5.4
- 70.3
Gemini 3 Pro
- 69.8 Claude Opus 4.5
- 69.5 Claude Sonnet 4.6
- 67.6 GPT-5.2
- 64.4 Kimi K2.5
- 63.1
Nemotron 3 Ultra
- 62.0
Gemini 3 Flash
- 59.5 Claude Sonnet 4.5
- 58.1 GLM-4.7
- 57.1
Gemini 3.1 Flash-Lite
- 56.7 GPT-5.4 Mini
- 50.1 GPT-5.1
- 44.5 o3-pro
- 40.2 Claude Haiku 4.5
- 1
Gemini 4 Argon 90.7%
- 2
Muse Spark 1.1 88.1%
- 3 Claude Fable 5.1 87.2%
- 4 Claude Opus 5 xhigh 85.8%
- 5 Claude Opus 5.5 85.4%
- 6
Inkling 2 Small 85.4%
- 7
Qwen3.8-2.4T-A95B xhigh 84.5%
- 8 GLM-5.3 84.2%
- 9
Gemini 3.5 Flash high 83.6%
- 10 Claude Fable 5 83.3%
- 11 Kimi K3 max 82.3%
- 12
Muse Spark 82.2%
- 13 Claude Opus 4.8 max 82.2%
- 14 GPT-5.6 Sol 81.8%
- 15
Inkling-Small 79.2%
- 16 Claude Opus 4.7 max 79.1%
- 17
Gemini 3.1 Pro high 78.2%
- 18 GLM-5.2 77.8%
- 19 Claude Opus 4.6 max 76.8%
- 20
Inkling xhigh 76%
- 21 GLM-5.1 75.6%
- 22 GPT-5.5 xhigh 75.3%
- 23 GPT-5.4 xhigh 70.6%
- 24
Gemini 3 Pro 70.3%
- 25 Claude Opus 4.5 high 69.8%
- 26 Claude Sonnet 4.6 69.5%
- 27 GPT-5.2 xhigh 67.6%
- 28 Kimi K2.5 64.4%
- 29
Nemotron 3 Ultra thinking 63.1%
- 30
Gemini 3 Flash 62%
- 31 Claude Sonnet 4.5 thinking 59.5%
- 32 GLM-4.7 58.1%
- 33
Gemini 3.1 Flash-Lite high 57.1%
- 34 GPT-5.4 Mini xhigh 56.7%
- 35 GPT-5.1 high 50.1%
- 36 o3-pro 44.5%
- 37 Claude Haiku 4.5 40.2%
Source: Official MCP Atlas leaderboard
- 90.7
Gemini 4 Argon
- 90.3
Muse Spark 1.2
- 89.2
Muse Spark Open
- 88.1
Muse Spark 1.1
- 87.2 Claude Fable 5.1
- 85.8 Claude Opus 5
- 85.6
Step 5 Preview
- 85.4 Claude Opus 5.5
- 85.4
Inkling 2 Small
- 84.5
Qwen3.8-2.4T-A95B
- 84.2 Kimi K3
- 84.2 GLM-5.3
- 83.8
Seed2.1 Pro
- 83.7
Tencent Hy4 preview
- 83.6
Gemini 3.5 Flash
- 83.3 Claude Fable 5
- 83.1
LongCat-2.5
- 83.0
Ling-3.1-flash
- 82.2
Muse Spark
- 82.2 Claude Opus 4.8
- 81.8 GPT-5.6 Sol
- 79.6
Inkling-Small
- 79.1 Claude Opus 4.7
- 79.1
Tencent Hy3
- 78.7 Beam 1.0
- 78.2
Gemini 3.1 Pro
- 77.8 GLM-5.2
- 76.8 Claude Opus 4.6
- 76.4
Qwen3.7-Max
- 76.0 Kimi K2.7 Code
- 76.0
Inkling
- 75.6 GLM-5.1
- 75.5
Muse Glimmer
- 75.3 GPT-5.5
- 74.2
MiniMax-M3
- 74.1
Qwen 3.6 Plus
- 73.6
DeepSeek-V4-Pro
- 73.2
Qwen3.7-Plus
- 70.6 GPT-5.4
- 70.3
Gemini 3 Pro
- 69.8 Claude Opus 4.5
- 69.5 Claude Sonnet 4.6
- 69.0
DeepSeek-V4-Flash
- 67.8 GLM-5
- 67.6 GPT-5.2
- 64.4 Kimi K2.5
- 63.1
Nemotron 3 Ultra
- 62.8
Qwen 3.6 35B-A3B
- 62.4
Spark-X2.6-4B
- 62.0
Gemini 3 Flash
- 61.4
Solar Pro 4
- 59.5 Claude Sonnet 4.5
- 58.2
Solar Open 2
- 58.1 GLM-4.7
- 57.7 GPT-5.4 Mini
- 57.1
Gemini 3.1 Flash-Lite
- 56.1 GPT-5.4 Nano
- 52.2
Nemotron 3.5 Bolt 74B-A9B
- 50.1 GPT-5.1
- 44.5 o3-pro
- 40.2 Claude Haiku 4.5
- 32.6 LFM3-1.7B
- 24.6
Nova 2 Lite
- 1
Gemini 4 Argon 90.7%
- 2
Muse Spark 1.2 90.3%
- 3
Muse Spark Open 89.2%
- 4
Muse Spark 1.1 88.1%
- 5 Claude Fable 5.1 87.2%
- 6 Claude Opus 5 xhigh 85.8%
- 7
Step 5 Preview 85.6%
- 8 Claude Opus 5.5 85.4%
- 9
Inkling 2 Small 85.4%
- 10
Qwen3.8-2.4T-A95B xhigh 84.5%
- 11 Kimi K3 84.2%
- 12 GLM-5.3 84.2%
- 13
Seed2.1 Pro 83.8%
- 14
Tencent Hy4 preview 83.7%
- 15
Gemini 3.5 Flash high 83.6%
- 16 Claude Fable 5 83.3%
- 17
LongCat-2.5 83.1%
- 18
Ling-3.1-flash 83%
- 19
Muse Spark 82.2%
- 20 Claude Opus 4.8 max 82.2%
- 21 GPT-5.6 Sol 81.8%
- 22
Inkling-Small 79.6%
- 23 Claude Opus 4.7 max 79.1%
- 24
Tencent Hy3 79.1%
- 25 Beam 1.0 78.7%
- 26
Gemini 3.1 Pro high 78.2%
- 27 GLM-5.2 77.8%
- 28 Claude Opus 4.6 max 76.8%
- 29
Qwen3.7-Max 76.4%
- 30 Kimi K2.7 Code 76%
- 31
Inkling xhigh 76%
- 32 GLM-5.1 75.6%
- 33
Muse Glimmer 75.5%
- 34 GPT-5.5 xhigh 75.3%
- 35
MiniMax-M3 74.2%
- 36
Qwen 3.6 Plus 74.1%
- 37
DeepSeek-V4-Pro 73.6%
- 38
Qwen3.7-Plus 73.2%
- 39 GPT-5.4 xhigh 70.6%
- 40
Gemini 3 Pro 70.3%
- 41 Claude Opus 4.5 high 69.8%
- 42 Claude Sonnet 4.6 69.5%
- 43
DeepSeek-V4-Flash 69%
- 44 GLM-5 67.8%
- 45 GPT-5.2 xhigh 67.6%
- 46 Kimi K2.5 64.4%
- 47
Nemotron 3 Ultra thinking 63.1%
- 48
Qwen 3.6 35B-A3B 62.8%
- 49
Spark-X2.6-4B 62.4%
- 50
Gemini 3 Flash 62%
- 51
Solar Pro 4 61.4%
- 52 Claude Sonnet 4.5 thinking 59.5%
- 53
Solar Open 2 58.2%
- 54 GLM-4.7 58.1%
- 55 GPT-5.4 Mini 57.7%
- 56
Gemini 3.1 Flash-Lite high 57.1%
- 57 GPT-5.4 Nano 56.1%
- 58
Nemotron 3.5 Bolt 74B-A9B 52.2%
- 59 GPT-5.1 high 50.1%
- 60 o3-pro 44.5%
- 61 Claude Haiku 4.5 40.2%
- 62 LFM3-1.7B 32.6%
- 63
Nova 2 Lite 24.6%
Sources: Official MCP Atlas leaderboard and labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 90.7% | Sep 2026 | Official leaderboard | |
| 2 | | 88.1% | Jul 2026 | Official leaderboard | |
| 3 | Claude Fable 5.1 | 87.2% | Sep 2026 | Official leaderboard | |
| 4 | Claude Opus 5 | 85.8% | xhigh | Jul 2026 | Official leaderboard |
| 5 | Claude Opus 5.5 | 85.4% | Sep 2026 | Official leaderboard | |
| 6 | | 85.4% | Nov 2026 | Official leaderboard | |
| 7 | | 84.5% | xhigh | Aug 2026 | Official leaderboard |
| 8 | GLM-5.3 | 84.2% | Aug 2026 | Official leaderboard | |
| 9 | | 83.6% | high | May 2026 | Official leaderboard |
| 10 | Claude Fable 5 | 83.3% | Jun 2026 | Official leaderboard | |
| 11 | Kimi K3 | 82.3% | max | Jul 2026 | Official leaderboard |
| 12 | | 82.2% | Apr 2026 | Official leaderboard | |
| 13 | Claude Opus 4.8 | 82.2% | max | May 2026 | Official leaderboard |
| 14 | GPT-5.6 Sol | 81.8% | Jul 2026 | Official leaderboard | |
| 15 | | 79.2% | Jul 2026 | Official leaderboard | |
| 16 | Claude Opus 4.7 | 79.1% | max | Apr 2026 | Official leaderboard |
| 17 | | 78.2% | high | Feb 2026 | Official leaderboard |
| 18 | GLM-5.2 | 77.8% | Jun 2026 | Official leaderboard | |
| 19 | Claude Opus 4.6 | 76.8% | max | Feb 2026 | Official leaderboard |
| 20 | | 76% | xhigh | Jul 2026 | Official leaderboard |
| 21 | GLM-5.1 | 75.6% | Apr 2026 | Official leaderboard | |
| 22 | GPT-5.5 | 75.3% | xhigh | Apr 2026 | Official leaderboard |
| 23 | GPT-5.4 | 70.6% | xhigh | Mar 2026 | Official leaderboard |
| 24 | | 70.3% | Nov 2025 | Official leaderboard | |
| 25 | Claude Opus 4.5 | 69.8% | high | Nov 2025 | Official leaderboard |
| 26 | Claude Sonnet 4.6 | 69.5% | Feb 2026 | Official leaderboard | |
| 27 | GPT-5.2 | 67.6% | xhigh | Dec 2025 | Official leaderboard |
| 28 | Kimi K2.5 | 64.4% | Jan 2026 | Official leaderboard | |
| 29 | | 63.1% | thinking | Jun 2026 | Official leaderboard |
| 30 | | 62% | Dec 2025 | Official leaderboard | |
| 31 | Claude Sonnet 4.5 | 59.5% | thinking | Sep 2025 | Official leaderboard |
| 32 | GLM-4.7 | 58.1% | Dec 2025 | Official leaderboard | |
| 33 | | 57.1% | high | Mar 2026 | Official leaderboard |
| 34 | GPT-5.4 Mini | 56.7% | xhigh | Mar 2026 | Official leaderboard |
| 35 | GPT-5.1 | 50.1% | high | Nov 2025 | Official leaderboard |
| 36 | o3-pro | 44.5% | Jun 2025 | Official leaderboard | |
| 37 | Claude Haiku 4.5 | 40.2% | Oct 2025 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 90.7% | Sep 2026 | Official leaderboard | |
| 2 | | 90.3% | Aug 2026 | Lab model card | |
| 3 | | 89.2% | Oct 2026 | Lab model card | |
| 4 | | 88.1% | Jul 2026 | Official leaderboard | |
| 5 | Claude Fable 5.1 | 87.2% | Sep 2026 | Official leaderboard | |
| 6 | Claude Opus 5 | 85.8% | xhigh | Jul 2026 | Official leaderboard |
| 7 | | 85.6% | Sep 2026 | Lab model card | |
| 8 | Claude Opus 5.5 | 85.4% | Sep 2026 | Official leaderboard | |
| 9 | | 85.4% | Nov 2026 | Official leaderboard | |
| 10 | | 84.5% | xhigh | Aug 2026 | Official leaderboard |
| 11 | Kimi K3 | 84.2% | Jul 2026 | Lab model card | |
| 12 | GLM-5.3 | 84.2% | Aug 2026 | Official leaderboard | |
| 13 | | 83.8% | Jun 2026 | Lab model card | |
| 14 | | 83.7% | Aug 2026 | Lab model card | |
| 15 | | 83.6% | high | May 2026 | Official leaderboard |
| 16 | Claude Fable 5 | 83.3% | Jun 2026 | Official leaderboard | |
| 17 | | 83.1% | Nov 2026 | Lab model card | |
| 18 | | 83% | Oct 2026 | Lab model card | |
| 19 | | 82.2% | Apr 2026 | Official leaderboard | |
| 20 | Claude Opus 4.8 | 82.2% | max | May 2026 | Official leaderboard |
| 21 | GPT-5.6 Sol | 81.8% | Jul 2026 | Official leaderboard | |
| 22 | | 79.6% | Jul 2026 | Lab model card | |
| 23 | Claude Opus 4.7 | 79.1% | max | Apr 2026 | Official leaderboard |
| 24 | | 79.1% | Jul 2026 | Lab model card | |
| 25 | Beam 1.0 | 78.7% | Oct 2026 | Lab model card | |
| 26 | | 78.2% | high | Feb 2026 | Official leaderboard |
| 27 | GLM-5.2 | 77.8% | Jun 2026 | Official leaderboard | |
| 28 | Claude Opus 4.6 | 76.8% | max | Feb 2026 | Official leaderboard |
| 29 | | 76.4% | May 2026 | Lab model card | |
| 30 | Kimi K2.7 Code | 76% | Jun 2026 | Lab model card | |
| 31 | | 76% | xhigh | Jul 2026 | Official leaderboard |
| 32 | GLM-5.1 | 75.6% | Apr 2026 | Official leaderboard | |
| 33 | | 75.5% | Aug 2026 | Lab model card | |
| 34 | GPT-5.5 | 75.3% | xhigh | Apr 2026 | Official leaderboard |
| 35 | | 74.2% | Jun 2026 | Lab model card | |
| 36 | | 74.1% | Apr 2026 | Lab model card | |
| 37 | | 73.6% | Apr 2026 | Lab model card | |
| 38 | | 73.2% | Jun 2026 | Lab model card | |
| 39 | GPT-5.4 | 70.6% | xhigh | Mar 2026 | Official leaderboard |
| 40 | | 70.3% | Nov 2025 | Official leaderboard | |
| 41 | Claude Opus 4.5 | 69.8% | high | Nov 2025 | Official leaderboard |
| 42 | Claude Sonnet 4.6 | 69.5% | Feb 2026 | Official leaderboard | |
| 43 | | 69% | Apr 2026 | Lab model card | |
| 44 | GLM-5 | 67.8% | Feb 2026 | Lab model card | |
| 45 | GPT-5.2 | 67.6% | xhigh | Dec 2025 | Official leaderboard |
| 46 | Kimi K2.5 | 64.4% | Jan 2026 | Official leaderboard | |
| 47 | | 63.1% | thinking | Jun 2026 | Official leaderboard |
| 48 | | 62.8% | Apr 2026 | Lab model card | |
| 49 | | 62.4% | Oct 2026 | Lab model card | |
| 50 | | 62% | Dec 2025 | Official leaderboard | |
| 51 | | 61.4% | Aug 2026 | Lab model card | |
| 52 | Claude Sonnet 4.5 | 59.5% | thinking | Sep 2025 | Official leaderboard |
| 53 | | 58.2% | Jul 2026 | Lab model card | |
| 54 | GLM-4.7 | 58.1% | Dec 2025 | Official leaderboard | |
| 55 | GPT-5.4 Mini | 57.7% | Mar 2026 | Lab model card | |
| 56 | | 57.1% | high | Mar 2026 | Official leaderboard |
| 57 | GPT-5.4 Nano | 56.1% | Mar 2026 | Lab model card | |
| 58 | | 52.2% | Oct 2026 | Lab model card | |
| 59 | GPT-5.1 | 50.1% | high | Nov 2025 | Official leaderboard |
| 60 | o3-pro | 44.5% | Jun 2025 | Official leaderboard | |
| 61 | Claude Haiku 4.5 | 40.2% | Oct 2025 | Official leaderboard | |
| 62 | LFM3-1.7B | 32.6% | Oct 2026 | Lab model card | |
| 63 | | 24.6% | Dec 2025 | Lab model card |
Labs report scores for 46 models in their cards. Switch the scores above to rank them together.
7 models have held number one on the official leaderboard since its release in January 2026 9 models have held number one with claims included since its release in January 2026
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Sources: Official MCP Atlas leaderboard and labs’ model cards
Muse Spark 1.1 on the official leaderboard passed 85% in July 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official MCP Atlas leaderboard and labs’ model cards
15 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Nov 5, 2026 | 85 | MCP Atlas (Public; 85.4 on Full) | |
| Nov 2, 2026 | 83.1 | MCP Atlas | |
| Oct 27, 2026 | LFM3-1.7B | 32.6 | MCP-Atlas |
| Oct 22, 2026 | 62.4 | MCP-Atlas | |
| Oct 15, 2026 | 89.2 | MCP-Atlas | |
| Oct 15, 2026 | 52.2 | MCP-Atlas (500 public tasks) | |
| Oct 13, 2026 | 83 | Mcp-altas | |
| Oct 5, 2026 | Beam 1.0 | 78.7 | MCP Atlas |
| Sep 20, 2026 | 85.6 | MCP-Atlas | |
| Aug 28, 2026 | 83.7 | MCP-Atlas (public) | |
| Aug 11, 2026 | 61.4 | MCP-Atlas* (in-house) | |
| Aug 10, 2026 | 75.5 | MCP Atlas (Public) | |
| Aug 5, 2026 | 90.3 | MCP-Atlas, 90.3% | |
| Jul 30, 2026 | 79.6 | MCP Atlas public 79.6 / all 79.2 | |
| Jul 22, 2026 | 58.2 | MCP-Atlas | |
| Jul 16, 2026 | Kimi K3 | 84.2 | MCP-Atlas |
| Jul 15, 2026 | 76 | MCP Atlas | |
| Jul 9, 2026 | 88.1 | MCP Atlas (Scaled tool use) | |
| Jul 2, 2026 | 79.1 | MCP atlas (public) | |
| Jun 16, 2026 | GLM-5.2 | 76.8 | MCP-Atlas (Public Set) |
| Jun 12, 2026 | Kimi K2.7 Code | 76 | MCP Atlas |
| Jun 1, 2026 | 73.2 | MCP-Atlas (public set) | |
| Jun 1, 2026 | 74.2 | MCP Atlas | |
| May 28, 2026 | Claude Opus 4.8 | 82.2 | MCP-Atlas |
| May 20, 2026 | 76.4 | MCP-Atlas | |
| May 19, 2026 | 83.6 | MCP Atlas | |
| Apr 24, 2026 | 69 | MCPAtlas (Pass@1), V4-Flash Max | |
| Apr 24, 2026 | 73.6 | MCPAtlas Public (Pass@1) | |
| Apr 23, 2026 | GPT-5.5 | 75.3 | MCP Atlas |
| Apr 16, 2026 | Claude Opus 4.7 | 77.3 | Scaled tool use MCP-Atlas |
| Apr 15, 2026 | 62.8 | MCP-Atlas (public set) | |
| Apr 7, 2026 | GLM-5.1 | 71.8 | MCP-Atlas (Public Set) |
| Apr 2, 2026 | 74.1 | MCP-Atlas | |
| Mar 17, 2026 | GPT-5.4 Nano | 56.1 | MCP Atlas |
| Mar 17, 2026 | GPT-5.4 Mini | 57.7 | MCP-Atlas |
| Mar 5, 2026 | GPT-5.4 | 67.2 | MCP Atlas |
| Feb 19, 2026 | 69.2 | MCP Atlas | |
| Feb 17, 2026 | Claude Sonnet 4.6 | 61.3 | Scaled tool use MCP-Atlas |
| Feb 11, 2026 | GLM-5 | 67.8 | MCP-Atlas (Public Set) |
| Feb 5, 2026 | Claude Opus 4.6 | 59.5 | Scaled tool use MCP Atlas |
| Dec 17, 2025 | 57.4 | MCP Atlas | |
| Dec 2, 2025 | 24.6 | MCP Atlas pass rate (blind eval) | |
| Nov 24, 2025 | Claude Opus 4.5 | 62.3 | Scaled tool use MCP Atlas |
21 system cards report it, from 11 labs
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 |
|---|---|---|---|---|
| Oct 19, 2026 | | 75.5 | MCPAtlas, 500 public tasks, high, 77.4 xhigh (Tables 2 and 5) | Capabilities |
| Oct 15, 2026 | | 52.2 | MCP-Atlas, public 500 tasks, reasoning on, released 24GB checkpoint (Table 7 and Section 8). BF16 55.60 | Capabilities |
| Jul 30, 2026 | | 79.6 | MCP Atlas public 79.6, all 79.2 (5. Evaluations table) | Capabilities |
| Jul 27, 2026 | Kimi K3 technical report | 84.2 | MCP-Atlas, 500-task public subset, Gemini 3.1 Pro judge (Table 2) | Capabilities |
| Jul 24, 2026 | Claude Opus 5 system card | 85.8 | MCP-Atlas pass rate (mean claim coverage 89.1%) (8.13.2) | Capabilities |
| Jul 22, 2026 | | 58.2 | MCP-Atlas (Table 3) | Capabilities |
| Jul 15, 2026 | | 76 | MCP Atlas (5. Evaluations table, effort 0.99) | Capabilities |
| Jul 9, 2026 | | 88.1 | MCP Atlas (Figure 44) | Capabilities |
| Jun 23, 2026 | | 83.8 | MCP-Atlas, Pass@1 (Table 2) | Capabilities |
| Jun 9, 2026 | Claude Fable 5 system card | 83.3 | MCP-Atlas pass rate (8.17.5) | Capabilities |
| May 28, 2026 | Claude Opus 4.8 system card | 82.2 | MCP-Atlas pass rate, run by Scale AI with a 100-tool-call budget, mean claim coverage 86.2% (Table 8.1.A, 8.13.4) | Capabilities |
| May 19, 2026 | | 83.6 | MCP Atlas (results table, p.4) | Capabilities |
| Apr 26, 2026 | | 73.6 | MCPAtlas Public (Pass@1), DS-V4-Pro Max (Table 6). High mode 74.2 (Table 7) | Capabilities |
| Apr 26, 2026 | | 69 | MCPAtlas Public (Pass@1), DeepSeek-V4-Flash Max (Table 7) | Capabilities |
| Apr 16, 2026 | Claude Opus 4.7 system card | 77.3 | MCP-Atlas pass rate, max effort, refreshed harness, run by Scale AI. Extended config 79.5 (Table 8.1.A, 8.10.3) | Capabilities |
| Feb 19, 2026 | | 69.2 | MCP Atlas (results table, p.4) | Capabilities |
| Feb 17, 2026 | GLM-5 technical report | 67.8 | MCP-Atlas (Public Set), Gemini 3 Pro judge, 10-minute timeout (Table 7) | Capabilities |
| Feb 17, 2026 | Claude Sonnet 4.6 system card | 61.3 | MCP-Atlas, max effort (Table 2.1.A, 2.14) | Capabilities |
| Feb 5, 2026 | Claude Opus 4.6 system card | 59.5 | MCP-Atlas, max effort (Table 2.3.A, 2.16); 62.7 at high effort | Capabilities |
| Dec 2, 2025 | | 24.6 | MCP Atlas pass rate, blind eval (Table 2) | Capabilities |
| Nov 24, 2025 | Claude Opus 4.5 system card | 62.3 | MCP Atlas, without extended thinking (Table 2.3.A, 2.12) | Capabilities |
The lab's number comes out ahead in 3 of 13 pairs that differ
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. 7 of them match, which usually means one side copied the other. Every benchmark.
| Model | Lab says | Board says | Gap |
|---|---|---|---|
| Claude Opus 4.6 | 59.5% Scaled tool use MCP Atlas | 76.8% Leaderboard, max | −17 pts |
| | 69.2% MCP Atlas | 78.2% Leaderboard, high | −9.0 pts |
| Claude Sonnet 4.6 | 61.3% Scaled tool use MCP-Atlas | 69.5% Leaderboard | −8.2 pts |
| Claude Opus 4.5 | 62.3% Scaled tool use MCP Atlas | 69.8% Leaderboard, high | −7.5 pts |
| | 57.4% MCP Atlas | 62% Leaderboard | −4.6 pts |
| GLM-5.1 | 71.8% MCP-Atlas (Public Set) | 75.6% Leaderboard | −3.8 pts |
| GPT-5.4 | 67.2% MCP Atlas | 70.6% Leaderboard, xhigh | −3.4 pts |
| Kimi K3 | 84.2% MCP-Atlas | 82.3% Leaderboard, max | +1.9 pts |
| Claude Opus 4.7 | 77.3% Scaled tool use MCP-Atlas | 79.1% Leaderboard, max | −1.8 pts |
| GPT-5.4 Mini | 57.7% MCP-Atlas | 56.7% Leaderboard, xhigh | +1.0 pts |
| GLM-5.2 | 76.8% MCP-Atlas (Public Set) | 77.8% Leaderboard | −1.0 pts |
| | 85% MCP Atlas (Public; 85.4 on Full) | 85.4% Leaderboard | −0.4 pts |
| | 79.6% MCP Atlas public 79.6 / all 79.2 | 79.2% Leaderboard | +0.4 pts |
| GPT-5.5 | 75.3% MCP Atlas | 75.3% Leaderboard, xhigh | Same |
| | 83.6% MCP Atlas | 83.6% Leaderboard, high | Same |
| Claude Opus 4.8 | 82.2% MCP-Atlas | 82.2% Leaderboard, max | Same |
| | 88.1% MCP Atlas (Scaled tool use) | 88.1% Leaderboard | Same |
| | 76% MCP Atlas | 76% Leaderboard, xhigh | Same |
| Claude Fable 5 | 83.3% MCP-Atlas pass rate (8.17.5) | 83.3% Leaderboard | Same |
| Claude Opus 5 | 85.8% MCP-Atlas pass rate (mean claim coverage 89.1%) (8.13.2) | 85.8% Leaderboard, xhigh | Same |
2 logged changes that move scores
Changes by the maintainers, so a score from before a change may not compare with one from after it. Newest first, each with the maintainers' own source. The list may be incomplete. What we log.
- Harness changed October 23rd, 2026
The maintainers enforced per-task tool restrictions during execution and stopped advertising disabled tools. Earlier runs could invoke tools outside the allowed set. Source
- Harness changed July 4th, 2026
The maintainers replaced the Python harness with a TypeScript agent loop and configurable run controls. Scores from the old and new harnesses require matching execution settings. Source
More in Tool use
- AutomationBench In use Agent completes end-to-end business workflows inside a simulated company exposing dozens of REST APIs. Graded on resulting state.
Gemini 4 Carbon 53.9%
- 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%
- Toolathlon-Verified In use 108 long-horizon tasks across 32 apps through about 600 MCP tools. Execution-based checkers inspect final environment state. GLM-5.3-Flash 78.4%
- DeepSearchQA In use Questions that need multi-step search and synthesis. Final answers graded against references. Kimi K3 95%
- 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%