Coding — AI That Writes Production Code

The new king of agentic coding. Anthropic's Mythos-class model doesn't just top the benchmarks — it rewrites them. SWE-Bench Pro 80.3% demolishes the field. FrontierCode Diamond 29.3% is 5× GPT-5.5. Stripe migrated 50 million lines of Ruby in a day. Token-efficient, vision-native, and built for the kind of long- horizon engineering work that separates tools from teammates.

SWE-Bench Pro 80.3% (SOTA — 21.7 points above GPT-5.5). FrontierCode Diamond 29.3% (5× GPT-5.5's 5.7%, 2× Opus 4.8's 13.4%). CursorBench SOTA. Senior Engineer Benchmark 91/100 (vs GPT-5.5's 62/100). 50M-line codebase migration in one day. Vision-only game completion. Claude Code integration. 1M context.

Premium pricing at $10/$50 per M tokens (2× Opus 4.8). Conservative safeguards route <5% of sessions to Opus 4.8 (cybersecurity, biology topics). Independent benchmarks still emerging. Usage limits during high demand on Pro/Max plans. Best experienced through Claude Code or compatible IDEs.

The agentic coding model that doesn't just autocomplete — it plans, tools up, debugs across files, and finishes the messy repo task while you walk the dog. Terminal-Bench 82.7% isn't a typo.

Terminal-Bench 2.0 82.7% (crushes Opus 4.7's 69.4%); Expert-SWE 73.1% on 20-hour human tasks; FrontierMath Tier 4 35.4%; ~40% fewer output tokens; 1M context with native tool use and Codex integration.

2× API price ($5/$30 per 1M tokens); trails Claude Opus 4.7 on SWE-Bench Pro (58.6% vs 64.3%); API not live at launch; early hallucination reports need verification.

The new gold standard for agentic software engineering — faster, more honest, and dramatically better at staying on track through complex, long-running tasks. SWE-Bench Pro 69.2% doesn't just beat every other model — it beats its own predecessor by nearly 5 points. Dynamic Workflows spawn hundreds of parallel agents. And a self-verification system that's 4× less likely to let buggy code slip through. This isn't an incremental update — it's the model Opus 4.7 should have been.

SWE-Bench Pro 69.2% (new SOTA — beats GPT-5.5's 58.6% and its own predecessor Opus 4.7's 64.3% by a massive margin). CursorBench strongest across all effort levels. 100% end-to-end on Super-Agent benchmark (only model to achieve this). Dynamic Workflows for large-scale codebase tasks. Same $5/$25 pricing as Opus 4.7. Available everywhere: Claude.ai, API, Bedrock, Vertex, GitHub Copilot.

Still premium-priced ($5/$25 per 1M tokens — same as 4.7, but output is cheaper than GPT-5.5's $30). Longer thinking traces on hard problems increase latency and token burn. Newer tokenizer may still inflate costs 15–35% on code-heavy prompts. Safety guardrails remain strict. GPT-5.5 still leads on Terminal-Bench (78.2% vs 74.6%). Best experienced in Claude Code or compatible IDEs.

Zhipu AI's open-weight coding model just took the #1 spot on Design Arena — the first open model to top that leaderboard. SWE-bench Pro 62.1%, Terminal-Bench 82.7 (Claude Code harness), FrontierSWE 74.4% (1% behind Opus 4.8). AkitaOnRails jumped it from 46/100 to 87/100 — the largest intra-family improvement ever recorded. MIT license, 1M context window built for long-horizon agent work, and two reasoning effort levels. The open-weight frontier just got real.

Design Arena #1 (Elo 1360 — surpassed Claude Fable 5), AkitaOnRails 87/100 Tier A (+41 points from GLM-5.1's 46), SWE-bench Pro 62.1% (beats GPT-5.5's 58.6% and Qwen 3.7 Max's 60.6%), Terminal-Bench 82.7 (Claude Code harness — edges Opus 4.8's 78.9), FrontierSWE 74.4% (1% behind Opus 4.8, 1% ahead of GPT-5.5). Highest-ranked open-source model across all long-horizon coding benchmarks. MIT license with no restrictions.

Still trails closed frontier leaders on several depth benchmarks — Opus 4.8 leads on SWE-bench Pro (69.2 vs 62.1), NL2Repo (69.7 vs 48.9), DeepSWE (58.0 vs 46.2). The 744B MoE architecture means local deployment requires serious hardware. No native vision capabilities. lmarena overall text arena placement is upper-mid (7th-10th range), not dominating general chat yet.