We evaluated models on Harvey’s Legal Agent Benchmark, which tests an agent’s ability to complete legal work. Each task asks an agent to answer a client inquiry using shell and file-editing tools, plus skills for Word, Excel, and PowerPoint.

• Claude Fable 5 ranks #1 at 11.25% with Claude Opus 4.8 fallback, followed by Claude Opus 4.8 at 9.58% and Claude Sonnet 4.6 at 5.00%. Without fallback, Fable 5 still ranks #1 at 10.42%.
• MiniMax-M3 is the top open-weight model at 4.17%, ahead of GPT 5.5 at 3.75%.
• Claude Fable 5 ran at $19.23 per test, compared with $10.22 for Claude Opus 4.8, $3.04 for Claude Sonnet 4.6, and $1.46 for MiniMax-M3.

We’ve released Code Migration, a proprietary benchmark that asks whether models can reimplement working programs in another language. Submissions are graded purely on behavior, the share of hidden tests the rebuilt program passes, across a CLI split (30 repositories migrated across Python, Java, Kotlin, Rust, and C++) and a COBOL-to-Java split.

• Claude Fable 5 leads CLI migration at 60.1%, well clear of Claude Opus 4.8 (40.1%) and GPT 5.5 (36.9%). Rebuilding command-line tools across five languages remains hard for the field.
• COBOL-to-Java is a much tighter race, with GPT 5.5 and Claude Opus 4.7 tied on top at 70.0% and several models close behind.
• GPT 5.5 is the cost-efficiency leader, reaching 45.2% overall at about $6 per task, roughly a fifth the cost of the comparably accurate Claude Opus 4.8 (47.2%) and the best accuracy-per-dollar at the frontier.

• We evaluated the open-weight Kimi K2.7 Code across our coding benchmarks, where it is the new #1 open-weight model on SWE-bench Verified (78.20%) and Terminal-Bench 2.1 (67.04%).

• Its SWE-bench score matches Claude Opus 4.6 (Thinking) and GPT 5.4 (xhigh), and narrowly edges GPT 5.3 Codex.

• On Vibe Code Bench, which tests full-stack app building from product specs through implementation and browser-based evaluation, Kimi K2.7 Code scores 47.21% and ranks #3 among open-weight models.

• It also scores 82.05% on LiveCodeBench, ranking #8 among open-weight models.

The headline is long-horizon coding: Kimi K2.7 Code is especially competitive on agentic SWE and terminal tasks, while LiveCodeBench is a relative weaker spot.

• We evaluated Anthropic’s new Claude Fable 5 across our benchmark suite. It resets the top of the leaderboard, taking #1 on the Vals Index (75.15%) and the Vals Multimodal Index (74.15%).

• Fable 5 topped most of the suite: #1 on CorpFin v2 (71.83%), MedScribe (88.52%), LegalBench (88.56%), MMLU Pro (91.50%), MMMU (89.31%), and ProofBench (77.00%).

• Coding is the headline. Claude Fable 5 is #1 on every coding benchmark with a scored Fable 5 result: Vibe Code Bench (90.35%), SWE-bench Verified (95.00%), Terminal-Bench 2.1 (80.52%), LiveCodeBench (89.78%), and IOI (72.25%).

• Vibe Code Bench is the standout: Anthropic now holds the top three spots, and Fable 5 leads the best non-Anthropic model by more than 20 points. Six months ago, no model cracked 20% on this benchmark; Fable 5 now reaches 90.35%.

• Compared with Claude Opus 4.8, Fable 5 improves by roughly 5 points on the Vals Index and roughly 8 points on Vibe Code Bench, even though Opus 4.8 took #1 just a week ago.

The API showed a high rate of refusals, especially on bio and cyber-related questions. Because of this, we ran Fable 5 with Claude Opus 4.8 as a fallback: if Fable 5 refused a task, Opus 4.8 handled the request instead. This mainly affected Terminal-Bench 2.1, GPQA, MMLU Pro, and MMMU.

The model has a 1M-token context window and 128k max output tokens. Evaluations were run with compute effort set to “max” and temperature set to 1.0.

Congrats to the Anthropic team on the release!