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Benchmark

Terminal Bench 2.1

Benchmark that tests AI agents on real command-line tasks in a terminal environment; version 2.1 of Terminal-Bench, run via the Harbor framework.

Known aliases

  • harborframework/terminal-bench-2.1
  • TerminalBench 2.1

Current stories

buildConfirmed2 publishers

GitHub bills HydraFusion by every model leg its router decides to call

The Copilot research preview picks a single, cascade, or critique workflow per request, and you pay standard Copilot rates for every token in every leg. So the router has to save more expensive inference than the extra calls cost.

Perspective Coverage

3 publishers
Builder
Builder 58%
Operator
Operator 28%
Investor
Investor 14%

Reality

Evidence55
Adoption
Not scored
Hype gap+30
Incentives70
Confidence60
buildOne report1 publisher

Requests to deepseek-v4-pro start returning V4.1-Flash on 14 September at 04:00 UTC

The model string keeps working after the cutover, so a pinned request comes back from V4.1-Flash. By the figures in the dev.to write-up, that model scores 90.6 on Terminal-Bench 2.1 and 42.3 on SimpleQA, against V4-Pro's 55.2.

Publishers:dev.to

Reality

Evidence38
Adoption40
Hype gap+30
Incentives70
Confidence45
leadershipOne report1 publisher

Meta's Muse Spark 1.3 matches three flagship models at 55 cents a task

A 61 on Artificial Analysis's index no longer requires flagship pricing. That changes what agent workloads should cost this quarter. The same release also grew dearer than its own predecessor and slipped on two evaluations.

Publishers:implicator.ai

Reality

Evidence68
Adoption22
Hype gap+18
Incentives58
Confidence55
buildOne report1 publisher

Coding agents cost $4,125 a month because 73% of it is context you already sent

A worked example on Anthropic's published August 2026 rates puts a five-agent fleet at $4,125 a month, nearly three quarters of it input. The post's own caching maths does not add up.

Publishers:dev.to

Reality

Evidence45
Adoption22
Hype gap+18
Incentives85
Confidence42
buildOne report1 publisher

Unsloth's 10% quant claim is really about which machines can run a 27B model

Dynamic 3.0 ships Qwen3.8-27B GGUFs from 6.2GB up, with an unreproduced accuracy claim attached. The number that matters is the one that decides where the file fits.

Publishers:runtimewire.com

Reality

Evidence34
Adoption45
Hype gap+28
Incentives74
Confidence41
buildOne report1 publisher

Ornith-1.0's benchmarks are fine. Ollama can't parse its tool calls.

A 9B MIT-licensed coding model that reportedly matches a 31B rival on SWE-Bench Verified is still unusable as a Claude Code backend, because the runtime never turns its tool-call XML into a file write.

Publishers:dev.to

Reality

Evidence44
Adoption18
Hype gap+38
Incentives24
Confidence52
buildOne report1 publisher

A harness gain is not a leaderboard win: reading the J-Space DeepSeek report properly

A viral X post said an inference-time text layer put DeepSeek V4 Pro ahead of Fable 5 on every task. The report it points to shows single runs, nine benchmarks, and two losses.

Publishers:runtimewire.com

Reality

Evidence40
Adoption18
Hype gap+55
Incentives62
Confidence45
buildOne report1 publisher

A 27B Apache-2.0 model in 17GB makes local inference a wiring decision, not a demo

Qwen 3.8 27B ran on a MacBook Pro from a 17GB GGUF and spent 21 minutes on one SVG. Licensing and access stopped being the blocker; latency and KV cache budgeting became the job.

Publishers:dev.to

Reality

Evidence48
Adoption58
Hype gap+18
Incentives62
Confidence52