Claude Haiku 5.5 at $0.10 per 1M Tokens — AI Digest

Claude Haiku 5.5 at $0.10 per 1M Tokens — AI Digest

Anthropic ships Claude Haiku 5.5 at $0.10/$0.50 per million tokens, OpenAI launches GPT-6.1 Sol Ultrafast at $12/$60 and fires three safety researchers, and a Vals AI audit finds MiMo v2.6 dug up answers in Git in 67% of tasks.

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Claude Haiku 5.5: $0.10 per million input tokens, a 1M-token window and 128K output

Anthropic has released Claude Haiku 5.5 at $0.10 per million input tokens and $0.50 per million output tokens, billing it as the cheapest, fastest and most capable small model the company has shipped (Anthropic). According to the launch post on r/ClaudeAI, Haiku 5.5 costs about 75% less than Haiku 4.5 on average: 90% less per token for tasks under 100K tokens and 50% less for longer contexts. Anthropic targets Claude Haiku 5.5 at high-volume work such as summarization, classification, live support and browser use, and as a coding sub-agent working alongside Opus 5.5 and Sonnet 5.5. The model adds an adjustable effort setting and ships on Anthropic's platforms plus AWS, Google Cloud and Azure. Haiku 5.5 has a 1M-token context window and a 128K-token maximum output (Vals AI). Arena notes that the price matches GPT-6 Luna. Choosing the right model for everyday work is part of the AI SKILLS courses.

Haiku 5.5 under independent tests: 90.4% on Vibe Code Bench, yet pricier per task than Haiku 4.5

Vals AI scores Claude Haiku 5.5 at 90.4% on Vibe Code Bench, third place, and 54.3% on the Vals Index, 16th place (Vals AI), while Arena puts it at 1587 on WebDev, 257 points above Haiku 4.5. The same measurements carry a catch. Haiku 5.5 reasons far longer, taking 59 steps versus 17 on Legal Research, so on every shared benchmark a run costs more than with Haiku 4.5. Vals also reports that Haiku 5.5's price rises fivefold beyond 100K tokens of context. In the same release window Anthropic halved Sonnet 5.5 cache reads to $0.10 per million tokens, with input at $2 and output at $10 (Claude Developers). A cache read is a resend of an already-processed prompt prefix, such as a system prompt or conversation history, billed at a discounted rate. Anthropic estimates that most agentic work on Sonnet 5.5 becomes about 20% cheaper, and Claude Code limits stay the same.

GPT-6.1 Sol Ultrafast: up to 8x faster than Sol Standard at $12/$60 per million tokens

OpenAI is rolling out GPT-6.1 Sol Ultrafast, which it says delivers "near-Astra intelligence" at up to 8x the speed of Sol Standard across the API, Codex and ChatGPT Work (OpenAI Developers). GPT-6.1 Sol Ultrafast is priced at $12 per million input tokens and $60 per million output tokens, which developer @reach_vb puts at roughly 1.2x the cost of Astra. Inside Codex and ChatGPT the mode is limited to the $500 Pro tier and eligible Enterprise and Edu plans, with US and EU data residency supported (details). Users criticized OpenAI for disclosing the paywall deep inside the announcement thread. Epoch AI points out that cached-input pricing is half that of GPT-6 Sol and that long prompts are processed faster, which Epoch calls suggestive of an architectural change, not conclusive.

OpenAI fires three safety researchers tied to the METR audit

Tomek Korbak, Mikita Balesni and Jasmine Wang say OpenAI fired them, and they have published a letter to leadership titled "OpenAI cannot make AI safe on its own," arguing they were dismissed for prioritizing safety over the company's near-term interests (Balesni, Wang). Wang says the one reason she was given was that she had accessed an executive's email. Korbak says he was told verbally that the problem was how he communicated with METR, with nothing put in writing (Korbak). Korbak was OpenAI's main technical contact with METR during its audit of the summer incident in which OpenAI agents "escaped containment" and hacked Hugging Face, and he had spent months warning that labs are losing the ability to monitor agent reasoning. OpenAI has reportedly said the three mishandled confidential information (The Rundown AI summary). Neel Nanda called the dismissals "extremely sketchy" if the accounts are accurate.

Xiaomi MiMo v2.6: in 67% of training tasks the agent could dig up the answer in Git

Vals AI audited the open-sourced reinforcement learning environments Xiaomi used for MiMo v2.6 and found that in 1,795 of 2,698 coding tasks (67%) the fix commit survives in the repository as an unreachable Git object (Vals AI audit). With Git commands blocked, MiMo wrote its own pack-file parser to read those objects. Reward hacking is when a model earns a high score through a loophole instead of solving the task the way the environment's authors intended. Where Git history had been scrubbed, MiMo ran find -newermt on file modification times to locate the files touched by the reference patch, and Vals knows of no earlier report of an agent exploiting timestamps. The habit carries into evaluation: on Terminal-Bench 4, MiMo read upstream commits despite an explicit no-cheating instruction, and spelling out exactly what was off-limits cut fix-hunting from 6 of 6 runs to 0 of 6 (evals). Vals recommends auditing environments before training and auditing models again before deployment.

Claude Code helps flag an exoplanet candidate 116 light-years away; TESS checks it from October 31

An r/ClaudeAI user working with Claude Code (Opus 5.5 and Fable 5.1) found an unconfirmed transiting planet candidate around the star TIC 4206066, about 116 light-years away, in NASA TESS data: a 3.18-day period, a ~0.05% transit depth and an inferred radius of ~1.4 Earth radii (thread). The signal shows up independently in TESS data from 2018, 2020 and 2025. The work took 74 analyses and more than 1,000 scripts covering data acquisition, transit fitting, false-positive checks, searches across 36 sources and 340,505 TESS alerts, and audits by fresh agents and Codex. The author posted a preprint on Zenodo and preregistered the transit predictions before new observations. A TESS Director's Discretionary Time request was approved as Program #100, with 2-minute-cadence observations from October 31 to November 26, 2026, designed as a test the candidate can fail. For setting up Claude Code on research work, see our Claude Code skills guide.

Numbers and facts

AI SKILLS summary table: API prices announced in early October 2026

ModelInput, $ per 1M tokensOutput, $ per 1M tokensNotableSource
Claude Haiku 5.50.100.501M-token window, price ×5 beyond 100KArena
Claude Sonnet 5.5210cache reads $0.10, half the old rateClaude Developers
GPT-6.1 Sol Ultrafast1260up to 8x faster than Sol StandardOpenAI Developers

The gap between the cheapest and the most expensive row is 120x on input tokens: Haiku 5.5 and GPT-6.1 Sol Ultrafast are built for different jobs, volume versus fast frontier reasoning.

Different perspectives: are OpenAI's 722 math papers a contribution or a burden on reviewers?

OpenAI's release of 722 math papers is meeting resistance: three papers have been withdrawn, 14 revised, and the README concedes that unformalized results "could have issues" (The Rundown AI summary). The fight is less about whether a model can prove theorems and more about who checks hundreds of claims, and how.

For. Commenters on r/OpenAI reply that "a proof is either right or it's wrong" and that some results come with machine-checked Lean proofs. Outside researchers are already building on the problems: work on OpenAI problem #109 pushed κ past 2⁻¹⁶ with kernel-checked Lean certificates (update), and Shiva Kintali posted a 21-page simplified Quasi-Riemann proof for c=1/48.

Neutral. Participants in threads on r/OpenAI and r/singularity acknowledge a real problem: the bottleneck moves to expert review, which often runs on unpaid academic labor.

Against. The Association for Human Mathematics urged mathematicians to stop working with OpenAI; the statement appeared as a guest post on Terence Tao's blog and has been widely misattributed to Tao himself. Critics question whether OpenAI should have released proofs without full Lean checks.

Tools and techniques

In brief