Benchmark profile
Terminal-Bench 3.0
A continuously maintained benchmark for difficult computer work, including coding, deep learning, finance, engineering, math, and science tasks.
How we use Terminal-Bench 3.0
We mirror the Terminal-Bench 3.0 version 0.1 table: 74 professional computer-work tasks across 7 domains. The Terminal-Bench and Harbor team first released this benchmark under the FrontierBench name.
Each row measures a model and agent harness together, including Codex, Claude Code, mini-SWE-agent, or Cursor CLI. We therefore keep the raw table display-only. The normalized result counts as one external agentic benchmark family, and the ranking pipeline requires another benchmark family and evaluator before it can affect a model score.
Tasks completed on Terminal-Bench 3.0 — August 11, 2026 snapshot
BenchLM mirrors the published tasks completed view for Terminal-Bench 3.0. Claude Opus 5 leads the public snapshot at 43.5% , followed by GPT-5.6 Sol (34.6%) and Claude Fable 5 (34.0%). We do not weight the raw table directly. A normalized version can contribute through our external agentic consensus gate.
Claude Opus 5
Anthropic
anthropic-opus-5-mini-swe-agent
GPT-5.6 Sol
OpenAI
openai-gpt-5-6-sol-codex
Claude Fable 5
Anthropic
anthropic-fable-5-claude-code
Tasks completed table (9 models)
ScoreThe published Terminal-Bench 3.0 snapshot places Claude Opus 5 first at 43.5%. The third row is 9.5 points behind. The broader top-10 range is 38.9 points, so the table still separates the published systems.
9 models have been evaluated on Terminal-Bench 3.0. The benchmark falls in the Agentic category. This category carries a 22% weight in BenchLM.ai's overall scoring system. Terminal-Bench 3.0 is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About Terminal-Bench 3.0
Year
2026
Tasks
74 professional computer-work tasks across 7 domains
Format
Task completion rate
Difficulty
Frontier autonomous knowledge work
Terminal-Bench 3.0, formerly FrontierBench, launched as version 0.1 with 74 tasks across seven domains. Each public row combines a model with an agent harness, so we keep the raw results display-only. Its normalized score counts as one external agentic benchmark family and cannot make a category eligible without independent corroboration.
BenchLM freshness & provenance
Version
Terminal-Bench 3.0 v0.1
Refresh cadence
Continuous
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
FAQ
What does Terminal-Bench 3.0 measure?
A continuously maintained benchmark for difficult computer work, including coding, deep learning, finance, engineering, math, and science tasks.
Which model leads the published Terminal-Bench 3.0 snapshot?
Claude Opus 5 currently leads the published Terminal-Bench 3.0 snapshot with 43.5% tasks completed. BenchLM keeps the raw table display-only. A normalized version can contribute through the external agentic consensus gate, which still requires independent corroboration.
How many models are evaluated on Terminal-Bench 3.0?
9 AI models are included in BenchLM's mirrored Terminal-Bench 3.0 snapshot, based on the public leaderboard captured on August 11, 2026 snapshot.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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