What is Terminal-Bench 2.0?

Terminal-Bench 2.0 is a benchmark that tests an AI model's ability to complete coding tasks in a real terminal environment — running commands, reading file system state, debugging, fixing errors. It's the most production-relevant benchmark for terminal-native coding agents (Claude Code, Codex CLI, OpenCode, Pi). Unlike SWE-Bench Verified (which tests patch generation), Terminal-Bench 2.0 tests full agent loops over multi-step terminal sessions.

Who leads Terminal-Bench 2.0 in May 2026?

Per DataCamp's May 2026 head-to-head test: GPT-5.5 leads at 82.7%, Claude Opus 4.7 at 69.4%, Gemini 3.1 Pro at 68.5%. The 13.3-point gap between GPT-5.5 and Opus 4.7 is significant — GPT-5.5 dominates terminal-loop benchmarks despite Opus 4.7 leading SWE-Bench Pro for production patches. Different benchmarks measure different things; pick by workload.

Should I use GPT-5.5 because it leads Terminal-Bench?

Only if your workflow is terminal-native long agent loops. GPT-5.5's lead reflects strong agentic training, tool-use reliability, and decision-making across multi-step tasks. If you're writing code patches that need to pass code review, Opus 4.7's SWE-Bench Pro lead (64.3% vs 58.6%) matters more. If you're cost-sensitive, Sonnet 4.7 or Gemini 3.1 Pro are 60-80% cheaper for similar quality on most tasks.

Where does Mythos Preview fit?

Anthropic's locked Claude Mythos Preview ranks #1 on BenchLM.ai's overall provisional leaderboard at 99/100. It leads SWE-Bench Pro at 77.8% and edges GPT-5.5 on OSWorld-Verified at 79.6%. But Mythos Preview is not procurable in May 2026 — it remains in restricted research access. For purchasing decisions, the Terminal-Bench 2.0 ranking among shipping models (GPT-5.5 > Opus 4.7 > Gemini 3.1 Pro) is what matters.

Quick Answer

Terminal-Bench 2.0: GPT-5.5 vs Opus vs Gemini (May 2026)

Published: May 3, 2026

Terminal-Bench 2.0 Results: GPT-5.5 vs Opus 4.7 vs Gemini 3.1 Pro (May 2026)

DataCamp ran Terminal-Bench 2.0 head-to-head on the three frontier coding models in late April 2026 and published results on May 1. GPT-5.5 dominates at 82.7% — a 13.3-point lead over Claude Opus 4.7 (69.4%) and a 14.2-point lead over Gemini 3.1 Pro (68.5%). For terminal-native coding agents, this changes the default model conversation.

Last verified: May 3, 2026

The numbers

Model	Terminal-Bench 2.0	SWE-Bench Pro	OSWorld-Verified	Notes
GPT-5.5	82.7%	58.6%	76.4%	Leads agentic terminal loops
Claude Opus 4.7	69.4%	64.3%	73.1%	Leads production patches
Gemini 3.1 Pro	68.5%	~60%	~71%	Leads coding-arena head-to-head
(Reference) Mythos Preview	~85%+ provisional	77.8%	79.6%	Not procurable
(Reference) GPT-5.4	~70%	~52%	~68%	Outdated, replaced by GPT-5.5

Source: DataCamp May 1 2026 Terminal-Bench 2.0 head-to-head; RevolutionInAI SWE-Bench Pro analysis; AISI / OSWorld eval.

What Terminal-Bench 2.0 actually tests

Terminal-Bench 2.0 (the v2 version released in early 2026) measures an agent’s ability to:

Run shell commands and interpret output. Does the agent correctly use ls, grep, find, git, npm, pytest?
Maintain state across multi-step tasks. Can it remember earlier commands in a 30+ turn session?
Debug and recover from errors. When a command fails, can it diagnose and try alternatives?
Use tools chained together. Can it pipe git log | grep | awk and reason about results?
Manage long-context terminal output. Can it handle 50K+ token build logs and find the relevant error?

This is different from SWE-Bench Verified (single-shot patch generation) and different from MMLU (knowledge tests). Terminal-Bench 2.0 is the closest public benchmark to “is this model usable as a terminal coding agent?”

Why GPT-5.5 leads

OpenAI’s training emphasis on agentic capability shows up here. Three drivers:

1. Tool-use training depth

GPT-5.5’s RLHF and post-training included extensive tool-use scenarios — running commands, reading output, recovering from errors. Anthropic and Google both train for tool use, but GPT-5.5’s reliability across long sequences is the highest in May 2026.

2. Long-running agent loop stability

Models drift over long agent loops — they forget instructions, get distracted by intermediate output, or refuse halfway through. GPT-5.5 holds focus better than Opus 4.7 on 30-50 turn sessions per DataCamp’s test methodology.

3. Pragmatic decision-making

GPT-5.5 makes “good enough” decisions faster. Opus 4.7 sometimes over-deliberates on simple terminal choices. For production workflows where speed matters, this is real time saved.

Why Opus 4.7 trails on Terminal-Bench but leads SWE-Bench Pro

The benchmark gap reflects design philosophy:

Opus 4.7 is trained for careful, reviewable code generation. It produces fewer regressions, cleaner diffs, more readable patches. SWE-Bench Pro (which measures real-world GitHub issue resolution) rewards this.
GPT-5.5 is trained for agentic completion. It’s more willing to try, fail, and try again — which works in terminal loops where iteration is cheap, but can produce messier patches in single-shot patch tasks.

Translation: GPT-5.5 finishes more terminal tasks; Opus 4.7 generates more reviewable code. Different jobs, different winners.

Where Gemini 3.1 Pro fits

Gemini 3.1 Pro at 68.5% on Terminal-Bench is competitive but not category-leading. Its real strengths are elsewhere:

Coding-arena head-to-head play. llm-stats.com flagged Gemini 3.1 Pro as the strongest in head-to-head coding-arena evaluations (where models compete on user-rated tasks).
Pricing. $2/$12 per million tokens vs GPT-5.5’s $5/$15 — 50-60% cheaper.
Long context. Strong 1M+ token performance.
Multimodal. Best vision + code + audio combined model.

For pure terminal-coding work, GPT-5.5 wins. For broader workloads at lower cost, Gemini 3.1 Pro is the most efficient choice.

Decision tree (May 2026)

Workflow	Best model
Long autonomous terminal loops (Codex CLI, Claude Code agent mode, OpenCode)	GPT-5.5
Production code patches that need to pass code review	Opus 4.7
Cost-sensitive, broad workload	Gemini 3.1 Pro or Sonnet 4.7
Multimodal (code + image + audio)	Gemini 3.1 Pro
500K-1M token reading	Opus 4.7
Coding-arena head-to-head play	Gemini 3.1 Pro
Already paying for ChatGPT Pro	GPT-5.5 (Codex CLI included)
Already paying for Claude Pro/Max	Opus 4.7 + Sonnet 4.7 routing

What this means for harness picks

The Terminal-Bench result reframes some harness recommendations:

Codex CLI users — your default model (GPT-5.5) is the Terminal-Bench leader. No reason to switch.
Claude Code users — Opus 4.7 is excellent at code review-quality patches but trails on terminal loops. Consider routing autonomous long-running tasks through OpenCode + GPT-5.5 if you can.
OpenCode users — model-routing is your superpower. Use GPT-5.5 for terminal loops, Opus 4.7 for patches. The vendor-freedom design pays off here.
Cline / Pi users — pick model per task. GPT-5.5 for autonomous; Opus 4.7 for careful work.

Mythos Preview — what to watch

Mythos Preview’s provisional ~85%+ Terminal-Bench score (BenchLM.ai) and 77.8% SWE-Bench Pro score signal Anthropic can match GPT-5.5 on agentic capability when it chooses to. The release timing question is the gating factor:

If Anthropic releases Mythos in Q3 2026: Anthropic re-takes the frontier coding lead.
If Anthropic delays: OpenAI’s GPT-5.5 / GPT-6 generation continues to define the frontier.

Watch Anthropic’s Q2 / Q3 announcements.

Pricing context (May 2026)

Model	Input ($/M tok)	Output ($/M tok)	Relative cost vs GPT-5.5
GPT-5.5	$5	$15	Baseline
Claude Opus 4.7	$15	$75	3-5x more expensive
Claude Sonnet 4.7	$3	$15	~Same as GPT-5.5
Gemini 3.1 Pro	$2	$12	50-60% cheaper

For Terminal-Bench-style workloads (lots of output tokens for code generation and tool use), Opus 4.7’s cost premium is real. Many teams in May 2026 use Sonnet 4.7 as the workhorse and reserve Opus 4.7 for the hardest 10-20% of tasks.

Bottom line

GPT-5.5 leads Terminal-Bench 2.0 at 82.7% — a meaningful 13-14 point lead over Opus 4.7 and Gemini 3.1 Pro. For terminal-native coding agents, GPT-5.5 is the new default. For production code patches that need code-review quality, Opus 4.7 still wins. For cost-sensitive broad workloads, Gemini 3.1 Pro and Sonnet 4.7 are the right picks. Most teams will route by task — and that’s fine.

Sources: DataCamp Terminal-Bench 2.0 head-to-head test May 1 2026, llm-stats.com leaderboard, BenchLM.ai Mythos Preview profile, RevolutionInAI SWE-Bench Pro analysis, AISI cyber eval May 1 2026, OpenAI / Anthropic / Google pricing pages May 2026.