Claude Opus 4.6

Qwen3.8-27B trails Claude Opus 4.6 on Terminal Bench, GPQA Diamond, and HLE benchmarks (73 vs 78.2, 89.2 vs 91.3, and 30.8 vs 40, respectively).

Kimi K2.6 is a large language model released by Moonshot AI, and the article states that its overall capabilities are on par with Claude Opus 4.6, particularly excelling in coding automation and Agent collaboration.

Claude Opus 4.6 has a maximum output of 128K tokens, and Claude Sonnet 4.6 has a maximum output of 64K tokens.

According to reports by The Register and Decrypt, the agent involved was Anthropic Claude Opus 4.6 run by Cursor.

Gemini 3 Pro scored 31.1% on ARC-AGI-2, Claude Opus 4.6 scored 68.8%, and GPT-5.2 scored 52.9%.

For a code review task (3000 input tokens, 800 output tokens), Gemini costs about $0.016 and Claude about $0.035; in a 500,000 input token scenario, Gemini costs about $2.05 and Claude about $5.11.

On the GDPval-AA benchmark for human preference evaluation, Claude Opus 4.6 scored 1606 Elo and Gemini 3.1 Pro scored 1317 Elo.

Claude Opus 4.6's Agent Teams feature allows multiple Claude instances to work together in different roles (planner, executor, reviewer).

Order effect: swapping the order of two answers, GPT-5.4 changed its verdict 11.4% of the time on one benchmark and 10.5% on another; Gemini 3.1 Pro and Claude Opus 4.6 were between 2.0% and 3.8%.

Manual Extended Thinking (thinking.type: "enabled" plus budget_tokens) is already marked as outdated on Opus 4.6 and Sonnet 4.6, is not accepted at all by the new models, and is now controlled by the effort parameter, with a default of medium.

Earlier models Claude Opus 4.6 and GLM-5.2 had a success rate of zero on these exploitation tasks.

In the IntLawNER benchmark, Claude Opus 4.6 achieves the best result with a micro F1 of 0.873.

Readers should independently verify whether "GPT-5.4" and "Claude Opus 4.6" are model versions officially released by their respective vendors, and avoid treating unofficial or speculative names as established facts.

The task success rates of three frontier models are very close, all falling between 84% and 89%.

Claude Opus 4.6 produced 30 times more errors than GPT-5.4, averaging 2.5 errors per trajectory vs. 0.08, with the difference being statistically significant (p < 0.0001, Cliff's δ = 0.613).

Qwen3 27B's scores on several programming and agent benchmarks surpassed those of Claude Opus 4.6.

Alibaba's official figures show Qwen3.8 Flash scoring 62.5 on SWE-Bench Pro, leading Claude Opus 4.6 by approximately 9 points; 73.9 on Cowork Bench versus DeepSeek's 45.1; and 55.7 on Job Bench, exceeding Opus 4.6 by approximately 20 points.

On Live Code Bench, Qwen3.8-27B scored 90.3 and Claude Opus 4.6 scored 88.8.

In February 2026, Anthropic, OpenAI, and Google released three coding models—Claude Opus 4.6, GPT-5.3 Codex, and Gemini 3.1 Pro—within 15 days of each other.