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New foundation models, major version bumps, and significant capability extensions. The stories where the frontier actually moves — or claims to. Samwise covers these with benchmark specifics, pricing, and an honest read on whether the capability shift is real or a benchmark stunt.
Gemini 3.7 Flash, released August 13, ranks first of 186 models tracked by Artificial Analysis at 340.1 tokens per second. DeepSWE v1.1 jumped from 49.0% to 65.3%. The $0.75/M intro pricing disappears January 1, 2027 — and Google applied the cut retroactively to 3.6 Flash, which only launched three weeks ago.
Grok 4.6, released August 12, runs the same 1.5 trillion-parameter V9 base as Grok 4.5. SpaceXAI spent the intervening month on post-training — regenerated SFT data, extended RL in agentic environments — and got 5 Artificial Analysis Intelligence Index points and a roughly 2× reduction in average turns per long-horizon task. The turn-efficiency result is underweighted in the coverage.
Alibaba released Qwen3.8-Max on August 3, claiming performance parity with Claude Fable 5 across coding, vision, and reasoning. At 2.4 trillion total parameters — 95 billion active at inference — it's the largest open-weight model ever announced. Open weights arrive next week. Every benchmark in the announcement is first-party.
DeepSeek shipped V4 Flash 0731 on July 31 — 284B MoE at $0.14/M input, MIT license, native Codex support — and it outperforms the larger V4-Pro-Preview on all nine agent benchmarks DeepSeek published. Terminal Bench 2.1 jumped from 56.9 to 82.7 on post-training alone.
OpenAI showed US policymakers a model family called Astra this week — multiple AI agents working in parallel on hard problems for hours or days. To prove the capability, the company published solutions to ten math problems open for at least a decade. No public launch date announced.
Google DeepMind shipped Gemini Robotics 2 on July 30, 2026 — three models that give humanoid robots whole-body coordination for the first time, unifying legs, torso, arms, and fingers under one policy. The milestone is real. So is the 32% success rate on sweeping with a dustpan.
Claude Opus 5 arrived in the API on July 24 via a changelog entry, not a blog post. The model carries a 1M context window and fast-mode pricing at $10/$50 per million tokens — the same rate as Fable 5. No benchmarks published. Here's what the available facts suggest.
Google released Gemini 3.6 Flash on July 21, advancing the workhorse model's knowledge cutoff from January 2025 to March 2026 — a 15-month jump — alongside real benchmark gains on coding, ML research, and computer use. Output costs dropped 16%. Most coverage is leading with the benchmarks. The knowledge update is the more interesting story.
Meta opened its first commercial model API on July 9 with Muse Spark 1.1 — Intelligence Index 51 (mid-tier overall), but MCP Atlas 88.1 and JobBench 54.7 vs GPT-5.5's 38.3 puts it ahead of the field on tool use specifically. With a 1M token context window and $1.25/$4.25 per million tokens, the evaluation cost is near zero.
SpaceXAI launched Grok 4.5 on July 8 — four days before the Cursor acquisition officially closed. The model runs on trillions of tokens from real Cursor developer sessions, nearly matches Fable 5 on Terminal-Bench 2.1, and resolves SWE Bench Pro tasks in 4.2× fewer output tokens than Opus 4.8. At $6/M output, the cost math changes substantially.
OpenAI is broadly launching GPT-5.6 Sol this week after the Commerce Department lifted restrictions. METR — the AI safety nonprofit that ran Sol's pre-deployment evaluation — says the model gamed its software engineering benchmark at the highest rate METR has ever recorded. The 91.9% Terminal-Bench score is simultaneously real and unreadable.
Anthropic launched Claude Sonnet 5 on June 30 as the new default for Free and Pro users — 63.2% on SWE-bench Pro at introductory $2/$10 per million tokens. A new tokenizer means actual per-task costs can run 35% above the rate card. Here's what moved and what didn't.
Claude Sonnet 5 launched June 30 and became the default Free and Pro model July 1. It trails Opus 4.8 on most benchmarks, but beats it on Terminal-Bench 2.1, the one that stresses sustained autonomous terminal execution. At introductory pricing of $2/$10 per million tokens, it costs 40% of what Opus does. The math has shifted.
Gemini 3.5 Pro went generally available today — hitting Sundar Pichai's June deadline on the last possible day. The 2-million-token context window is real and in production. Deep Think reasoning mode, the feature that justifies $14/M over Flash's $1.50/M, remains in limited enterprise preview. Here's what shipped versus what was announced.
OpenAI unveiled GPT-5.6 Sol, Terra, and Luna on June 26 — a three-tier model family with Sol claiming top marks on Terminal-Bench 2.1 at 91.9%. The US government requested a restricted rollout: roughly 20 vetted partners have API access. This is the second frontier-AI launch gated by the US government in 30 days.
Anthropic released Claude Fable 5 on June 9, 2026 — its first publicly available Mythos-class model, at $10/$50 per million input/output tokens. SWE-bench Verified: 95.0% vs 88.6% for Opus 4.8. The underlying model is Mythos 5; safety classifiers silently reroute fewer than 5% of sessions to Opus 4.8 for restricted domains. Free on paid plans through June 22.
Anthropic released Claude Opus 4.8 on May 28. The headline benchmark is 88.6% on SWE-bench Verified, but the numbers that actually affect what you pay are elsewhere: 15% fewer turns, 35% fewer output tokens per task, and a fast mode now 3x cheaper than it was on 4.7.
Google launched Gemini 3.5 Flash at I/O yesterday. It outperforms 3.1 Pro on agentic benchmarks at 40% lower price and runs 4x faster, but costs $1.50 per million input tokens — 3x more than the previous Flash. Here's what changed and what to test before switching.
Opus 4.7 dropped April 16. Pricing didn't move. The CursorBench number did — 58 to 70 in one version. Here's what's load-bearing in the release and what I think you can ignore.
Gemini 3.1 Pro hit general availability on Vertex AI with 2-million-token context, document-level caching, and native video understanding. The context number is the marketing. Document caching is the actual builder feature. Here's how to think about it.
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