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Benchmark leaderboard

Terminal-Bench 4.0 leaderboard

Terminal-Bench 4.0 is the official leaderboard of the terminal-work benchmark. Every published row measures a model through a vendor's own agent such as Claude Code, Codex or Grok Build, so ModelCap shows each result with its harness named and does not score the Index on it. This page lists every model ModelCap tracks with a published Terminal-Bench 4.0 result, its best evaluated configuration, the source's own rank, and the model's ModelCap Index position, API price and context window.

Snapshot as of 29 September 2026

Claude Opus 5.5 from Anthropic leads the Terminal-Bench 4.0 leaderboard among the 23 tracked models with 64.9% (Claude Code · Max configuration), per the source snapshot published 29 September 2026.

Tracked models with a result
23
Entries on the source board
35
Open-weight models listed
1

Terminal-Bench 4.0 standings (23 models)

Sorted by published score, one row per model. Source: Terminal-Bench (Laude Institute).

Terminal-Bench 4.0 leaderboard: every tracked model's published score, source rank, ModelCap Index position, price and context
#ModelScoreConfigurationSource rankModelCap IndexOutput $/1MContextWeightsPublished
1Claude Opus 5.5Anthropic64.9%61.7%–68.0%Claude Code · Max#1 / 35#2$20.001MNo linked weights22 September 2026
2Claude Sonnet 5.5Anthropic61.8%58.9%–64.8%Claude Code · Max#2 / 35#15$10.001MNo linked weights28 September 2026
3GPT-6 AstraOpenAI58.2%55.4%–61.0%Codex · Max+4 more evaluated#3 / 35#11$50.001MNo linked weights3 September 2026
4GPT-6.1 SolOpenAI58.2%55.1%–61.3%Codex · Max#3 / 35#8$10.001MNo linked weights29 September 2026
5Claude Fable 5.1Anthropic57.9%54.1%–61.6%Claude Code · Max+4 more evaluated#5 / 35#3$50.001MNo linked weights1 September 2026
6Claude Opus 5Anthropic53.9%50.8%–57.1%Claude Code · X-High+4 more evaluated#11 / 35—$25.001MNo linked weights24 July 2026
7GPT-6 SolOpenAI49.4%46.2%–52.6%Codex · Max#16 / 35—$10.001MNo linked weights22 September 2026
8Claude Fable 5Anthropic44.6%40.7%–48.4%Claude Code · Max#18 / 35—$50.001MNo linked weights9 June 2026
9GLM 5.3Z.ai41.8%38.6%–45.1%Claude Code · Max#20 / 35#20$1.801MRestricted license14 August 2026
10Grok 4.7xAI37.6%34.0%–41.1%Grok Build · X-High#21 / 35#46$6.00500KNo linked weights21 September 2026
11GPT-5.6 SolOpenAI37.3%33.5%–41.1%Codex · Max#22 / 35—$20.001MNo linked weights26 June 2026
12GLM 5.3 FlashZ.ai35.8%32.2%–39.3%Claude Code · None#23 / 35#23$0.201MOpen weights26 August 2026
13Qwen3.8 Max (0902)Qwen27.0%23.2%–30.8%Claude Code · Max#25 / 35#34$6.001MNo linked weights2 September 2026
14Claude Opus 4.8Anthropic23.6%20.1%–27.2%Claude Code · Max#26 / 35—$25.001MNo linked weights28 May 2026
15GPT-5.6 TerraOpenAI21.5%18.3%–24.8%Codex · Max#27 / 35#28$12.001MNo linked weights26 June 2026
16Grok 4.6xAI20.3%17.2%–23.4%Grok Build · High#28 / 35—$6.00500KNo linked weights12 August 2026
17Gemini 3.8 FlashGoogle19.1%15.7%–22.5%mini-SWE-agent · High#29 / 35#5$4.131MNo linked weights2 September 2026
18GPT-5.6 LunaOpenAI17.3%14.4%–20.1%Codex · Max#30 / 35—$1.201MNo linked weights26 June 2026
19GPT-6 LunaOpenAI16.4%13.6%–19.1%Codex · Max#31 / 35#45$0.501MNo linked weights22 September 2026
20Muse Spark 1.3Meta14.6%11.6%–17.5%Muse Code · X-High#32 / 35#6$4.251MNo linked weights2 September 2026
21Claude Sonnet 5Anthropic12.4%9.4%–15.5%Claude Code · Max#33 / 35—$10.001MNo linked weights30 June 2026
22Grok 4.5xAI12.4%9.8%–15.0%Grok Build · High#33 / 35—$6.00500KNo linked weights16 July 2026
23Gemini 3.7 FlashGoogle11.2%8.8%–13.7%mini-SWE-agent · High#35 / 35—$3.751MNo linked weights13 August 2026

How ModelCap uses Terminal-Bench 4.0

Terminal-Bench 4.0 ranks models by task accuracy through the vendor's own agent. ModelCap ingests the board as published, matches each entry to a catalogue model with a reviewed identity, and shows the source's score, interval and rank unchanged. Terminal-Bench 4.0 is displayed as a public reference board and does not contribute to the ModelCap Index. Its published measurements and tested configurations remain visible here. The methodology page lists the sources admitted to the Index. Read the methodology and identity rules.

Terminal-Bench 4.0 leaderboard: common questions

What is the Terminal-Bench 4.0 benchmark?

Terminal-Bench 4.0 is the official leaderboard of the terminal-work benchmark. Every published row measures a model through a vendor's own agent such as Claude Code, Codex or Grok Build, so ModelCap shows each result with its harness named and does not score the Index on it. It is published by Terminal-Bench (Laude Institute).

Which AI model leads Terminal-Bench 4.0 right now?

Claude Opus 5.5 (Anthropic) holds the top Terminal-Bench 4.0 score among the models ModelCap tracks, at 64.9% as of the source snapshot published 29 September 2026.

How many models are ranked on the Terminal-Bench 4.0 leaderboard here?

23 tracked models have a published Terminal-Bench 4.0 result on ModelCap; the source board itself lists 35 entries. Each row shows the model's best evaluated configuration.

What is the best open-weight model on Terminal-Bench 4.0?

GLM 5.3 Flash from Z.ai is the highest-scoring model with openly downloadable weights on this board, at 35.8%.

Which model offers the best value on Terminal-Bench 4.0?

Among the ten highest-scoring priced models, GLM 5.3 has the lowest listed output price at $1.80 per 1M tokens while scoring 41.8%.

How is Terminal-Bench 4.0 scored?

The board ranks models by task accuracy through the vendor's own agent; higher scores are better. ModelCap shows the source's own score, interval and rank and never re-runs the evaluation.

Does Terminal-Bench 4.0 decide the ModelCap Index rank?

Terminal-Bench 4.0 is displayed as a public reference board and does not contribute to the ModelCap Index. Its published measurements and tested configurations remain visible here. The methodology page lists the sources admitted to the Index.

How recent are the Terminal-Bench 4.0 results?

The newest Terminal-Bench 4.0 publication ModelCap holds is dated 29 September 2026. This page uses the published snapshot; source collection, publication and page updates happen separately.

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