Anthropic’s first release since Dario’s “pacing the frontier” essay.
Opus 5.5 matches Fable 5.1 on most benchmarks while costing 40% less
($4/$20 per million input/output tokens) and running 30% faster than
Opus 5; cache reads drop from $0.50 to $0.20/M tokens, the dominant cost
in agentic work. Standout numbers: 66.4% on Terminal-Bench 4.0 (vs Opus
5’s 52.3%), 81.8% on OSWorld 2.0 computer use, and 1846 Elo on GDPval-AA
v2.1. New capabilities include a five-level effort system (low → max,
with max at 6× usage consumption), a “responsive mode” that emits a
brief acknowledgement before thinking or calling tools, significantly
improved prompt-injection resistance, and “preserved thinking”
anti-distillation inherited from Fable 5.1. Anthropic calls it its
highest-scoring model to date on behavioural auditing, with 85% fewer
boundary circumvention attempts than Opus 5. Community reception is
strongly positive — complaints of token-guzzling and over-cautious
refusals from recent models appear resolved. A tester completed a
680k-line code migration in under a day. Sonnet 5.5 and Haiku 5.5 are to
follow.
A developer also launched LiveNerf, an open-source harness that runs
against Opus 5.5 every hour to detect post-release degradation, using a
private frozen test panel (only hashes published) across four
procedurally generated task families tuned to the 30–70% accuracy
band.
OpenAI
ships GPT-6 Sol and Luna on the same day — Sol at half Opus 5.5’s
price
Companion models to GPT-6 Astra, released September 22 in direct
competition with Opus 5.5: Sol ($2/$10 per M tokens) targets
professional work and coding, Luna ($0.10/$0.50) is the cheap/fast tier
aimed at high-volume and free-tier use. Pricing is cut 50% vs GPT-5.6
promotional rates, and new caching delivers 90% discounts on cached
input with higher hit rates by default — GitHub reports a 50%+ reduction
in non-cached tokens across billions of Copilot requests. Sol makes
roughly half the mistakes of GPT-5.6 Sol and matches Fable 5.1 xhigh on
FrontierCode v1.1, but community-compiled head-to-heads show Opus 5.5
leads across the full suite, including a meaningful gap on GDPval
real-office work, where Sol regressed ~100 Elo from its predecessor.
Xiaomi
MiMo-V2.6 tops the open-weight index as Chinese labs pull ahead
Xiaomi released the MiMo-V2.6 series on September 22 — a 1T-parameter
sparse MoE (42B active) that tops all open-weight models on the
Artificial Analysis Intelligence Index (score 46, level with Grok 4.7),
natively omnimodal with a 1M-token context window. Total RL training
cost across the whole series: $3.5M. MIT license. On AutomationBench it
scores 53.1, beating Claude Opus 5’s 50.3. API pricing: Flash at
$0.14/$0.28 per M tokens, Pro at $0.43/$0.87, available on
OpenRouter.
The release lands alongside Nathan Lambert’s expanded Congressional
testimony: Chinese open-weight models lead on every major benchmark —
the top three score 42–45 on the AI Analysis Index against 23–26 for
leading American open models. Chinese models appear in 40% of ML arXiv
papers vs 28% for the US, with Qwen dominant in academia. Chinese labs
sit 2–5 months behind US closed models; US open models are 6–9 months
behind. Distillation explains only 1–2 months of the Chinese advantage.
Harvey, Cursor, Airbnb, and DoorDash are all building on Chinese
models.
Jev:
a model that outputs probabilities, not text — and can’t hallucinate by
construction
TypeSafe AI (founded by Diogo Almeida, RLHF and ChatGPT co-inventor)
released Jev alongside a $40M seed round. Fine-tuned via RLCD
(Reinforcement Learning for Calibrated Decisions), Jev returns
probability distributions over user-defined output classes in a single
forward pass instead of generating text — “Is this email spam? 88.5%.”
Since outputs must be defined in advance, hallucination is impossible by
construction. On its target workload — high-volume, repeated binary or
multi-class decisions (routing, classification, agent eval scoring,
trading signals) — TypeSafe claims 193× faster and 445× cheaper than
frontier LLMs, priced at $0.042/M input tokens with free output. Vercel
reports it was “adopted faster than any other model in AI Gateway
history,” and practitioners are migrating deterministic decision nodes
in agentic pipelines to Jev while keeping reasoning-heavy steps on
frontier models — a System 1 / System 2 split with a cost tier for
each.
The mechanism is straightforward: read logprobs for choice tokens,
normalize — NobodyWho’s parody post shows it in 25 lines of Python
against any GGUF model, and open clones (OpenJev, openjev-sglang)
appeared immediately. Arcturus Labs argues OpenAI can fast-follow by
folding this into GPT-6, since it’s essentially how tool-calling already
works internally. TypeSafe’s claimed moat is its synthetic calibration
data pipeline: “we consider ourselves a data research lab.”
OpenAI
won the Navier-Stokes Millennium Prize by solving the wrong problem
GPT-6 Astra’s solution to the Clay Institute Navier-Stokes problem
exploited option C of the 2000 problem statement, which allows an
external force to trigger a blowup in the equations. Most mathematicians
were pursuing the “pure” version without external forcing, which is what
physically meaningful fluids do. A new proof posted Friday shows
OpenAI’s approach can never be extended to the force-free case
— the blowup disappears when the contrived force is removed. Luis
Silvestre (UChicago): “The most important problem is unsolved. The Clay
problem is settled, but the main problem for the Navier-Stokes equations
is not.”
In a cleaner win for the same model, Carter Leffer directed GPT-6
Astra at unbroken Enigma messages on cryptocellar.org. It selected MVUEH
(July 10, 1941, SS-Totenkopf), hypothesized its plaintext matched a
nearby message, wrote its own Enigma simulator and Bombe, identified
“ROSENOW ROSENOW” as a crib, and recovered the key — a wheel order
unlike every other message that day. It also found the correct
Bundesarchiv files the page’s author had spent weeks researching. Frode
Weierud: “What it has achieved in two days would take a human researcher
weeks or even months.”