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Benchmark

Terminal Bench 2

A benchmark that tests AI agents on realistic terminal-based coding and system tasks, used to measure task-completion accuracy of language model agents.

Known aliases

  • TB 2.0
  • Terminal Bench 2.0

Current stories

investOne report1 publisher

Meta, Duke and UC Davis researchers lift Gemini 3 Flash to 62% on Olympiad math with a better harness

Meta, Duke and UC Davis researchers lifted Gemini 3 Flash from 46% to 62% on Olympiad math by searching for a better harness around the same model. That puts the wrapper on an operator's budget ahead of a bigger model, at least for math.

Reality

Evidence35
Adoption
Not scored
Hype gap+20
Incentives
Not scored
Confidence40
buildOne report1 publisher

Swapping the scaffold moved Claude Opus 4.5 from 42% to 78% on CORE-Bench

The same model scored twice under two scaffolds. A dev.to post uses that gap to argue the dividing line in AI coding is whether the model can run your repo's own commands and read the failure.

Publishers:dev.to

Reality

Evidence45
Adoption
Not scored
Hype gap+18
Incentives45
Confidence50
productOne report1 publisher

Antigravity gives away five frontier models on a quota Google keeps trimming

Antigravity's free tier includes Gemini 3.1 Pro and Claude Opus 4.6 with unlimited tab completions. XDA reports Opus burns credits about four times faster than Gemini, and Google moved the free tier onto credits in March 2026.

Reality

Evidence36
Adoption
Not scored
Hype gap+12
Incentives58
Confidence40
buildOne report1 publisher

A memory agent that keeps the option to stay silent lifts Terminal-Bench 2.0 pass@1 by 8.3 points

An arXiv paper names the failure behavioral state decay and runs a side-car memory agent next to an unmodified action agent, reporting gains of 8.3 and 6.8 points of pass@1 on two long-horizon benchmarks.

Publishers:arxiv.org

Reality

Evidence52
Adoption
Not scored
Hype gap+18
Incentives60
Confidence57
buildOne report1 publisher

Deep Agents now swaps in apply_patch the moment you name a Codex model

LangChain's new harness profiles set prompts, tool implementations and tool names per model family. The company measures a 10 to 20 point gain on a tau2-bench subset it curated from tasks frontier models have not saturated.

Publishers:langchain.com

Reality

Evidence45
Adoption15
Hype gap+18
Incentives80
Confidence55
investOne report1 publisher

A $14.34 router matched Opus-5's score on LiteLLM's 21-task benchmark

LiteLLM's own Terminal-Bench run puts a gpt-5.4-mini classifier routing across Haiku, Sonnet and Opus at $14.34 against Opus-5's $19.74 for the same 16 solved tasks. The saving works out at 26 cents a task.

Publishers:docs.litellm.ai

Reality

Evidence45
Adoption15
Hype gap+35
Incentives80
Confidence48
buildOne report1 publisher

Cached prefixes push 91% of a ReAct agent's LLM time into decode

An arXiv tracing study of Claude Code agents on Gemma and Qwen measured prefix-cache hit rates between 84.6 and 99.5 percent, which moves the serving bottleneck to how long you can keep KV blocks resident between tool calls.

Publishers:arxiv.org

Reality

Evidence58
Adoption25
Hype gap+12
Incentives
Not scored
Confidence55
buildOne report1 publisher

Experiential Labs bets its open-source router's traces will train cheaper replacements for rented models

The router picks a cheaper model for work that resembles work it has already seen, and falls back to the strongest one when nothing matches. The same traces that enable that call are the proposed training set.

Publishers:runtimewire.com

Reality

Evidence34
Adoption17
Hype gap+41
Incentives74
Confidence52