Is Terminal-Bench 4.0 still meaningful?
Status:Flawed Checked against the report, 5 November 2026
Verdict
Use with care. It is a launch-table favourite, but it is rated Flawed and lab numbers disagree.
Why flawed: Epoch AI rated Terminal-Bench 4.0.0 Flawed on 4 September 2026.[1][1] Terminal-Bench 4.0 review Epoch AI Primary
What “Flawed” meansKnown problems
- It has about 10 false-positive and 6 false-negative tasks (secondary detail).[1][1] Terminal-Bench 4.0 review Epoch AI Primary
- Anthropic reports Opus 5.5 at 66.4% and GPT-6 Astra at 57.9%. Artificial Analysis puts Kimi K3.1 Pro first at 67.2%.[3][3] Introducing Claude Opus 5.5 Anthropic Vendor[2][2] Terminal-Bench 4.0 Artificial Analysis Primary
- Version 2.0 is replaced: its top score was 84.7% (Apr 2026), and the live site shows only 4.0.[4][4] Terminal-Bench 2.0 leaderboard Primary
- UK AISI found that a rise from 1 to 10 million tokens raised Terminal-Bench 2.0 scores by about 25%.[5][5] More compute, more capability UK AISI Primary
- Chinese cards often run Western agent tests on Claude Code or Codex harnesses.[6][6] DeepSeek-V4.1-Flash model card Primary[7][7] GLM-5.3 model card Primary Some report their best score across harnesses.[8][8] Qwen3.8-Flash-Next-FP8 model card Primary
What to look at instead
The report names no direct successor.
Where it appears in the report
Chapter 1, Benchmarks
- The harness and the host can move a score more than the model
- Figure 4. Best score on benchmarks that still resist, November 2026
- Figure 5. Thirteen Flawed verdicts in ten weeks
- Table 2. First model to cross each threshold in “Go deeper: The full saturation timeline (14 benchmarks)”
- Table 4. Epoch AI Benchmark Review verdicts and the main defects found, August to October 2026 in “Go deeper: Epoch's verdicts, benchmark by benchmark”
- Table 6. Coding and software-agent benchmarks, November 2026 in “Go deeper: Coding and software-agent benchmarks (14)”
- Figure 14. OpenAI and Anthropic share only five headline benchmarks
Chapter 2, Evaluation science
Chapter 3, Tools and technology
- Two harnesses carry model benchmarking, and Harbor carries agent tasks
- Eval environments and RL training environments merged into one layer
Chapter 6, China and Asia
Sources
Copied from the chapter source lists. Each number links back to the source in its chapter.
- [1] Terminal-Bench 4.0 review, Epoch AI. The Flawed verdict for version 4.0.0. Primary epoch.ai Chapter 1, source 7
- [2] Terminal-Bench 4.0, Artificial Analysis. It gives independent scores on one shared harness. Primary artificialanalysis.ai Chapter 1, source 13
- [3] Introducing Claude Opus 5.5, Anthropic. It gives Anthropic's launch table and caveats. Vendor anthropic.com Chapter 1, source 12
- [4] Terminal-Bench 2.0 leaderboard. It gives version 2.0 scores by agent and model. Primary tbench.ai Chapter 1, source 43
- [5] More compute, more capability, UK AISI. It shows how token budgets change agent scores. Primary aisi.gov.uk Chapter 2, source 10
- [6] DeepSeek-V4.1-Flash model card. It gives base and chat benchmark tables and a harness-sensitivity table. Primary huggingface.co Chapter 6, source 4
- [7] GLM-5.3 model card. It reports Z.ai's benchmarks against Western and Chinese rivals. Primary huggingface.co Chapter 6, source 5
- [8] Qwen3.8-Flash-Next-FP8 model card. It reports Alibaba's benchmarks and its best-of-harness rule. Primary huggingface.co Chapter 6, source 34