Anthropic ships Claude Opus 4.7 as coding improves and the tokenizer quietly reshapes the bill
The per-token rate holds at $5 input and $25 output — but the same prompt now costs up to 35% more under the updated tokenizer.
The release is a routine point upgrade and a silent repricing at once. Opus 4.7 landed on April 16 across Anthropic's own surface, the Claude API, Amazon Bedrock, Google's Vertex AI, and Microsoft Foundry — the full hyperscalerA massive cloud service provider, typically Amazon Web Services, Microsoft Azure, or Google Cloud, capable of provisioning computing infrastructure at a global scale. distribution unchanged from 4.6, and priced identically at $5 per million input tokens and $25 per million output. The substantive change is not in the price card. It is in the tokenizerThe component of a language model that splits raw text into the smaller units (tokens) the model actually processes. Changes to a tokenizer shift how many tokens a given piece of text consumes, which affects both serving cost and the shape of the model's input., which Anthropic updated beneath the release and which now expands the same prompt by a factor of roughly 1.0 to 1.35 depending on content type.
For most of the past eighteen months, the frontier-model pricing debate has hinged on the per-token rate. Labs compressed it slowly, in staged cuts designed to be read as competition working. The tokenizerThe component of a language model that splits raw text into the smaller units (tokens) the model actually processes. Changes to a tokenizer shift how many tokens a given piece of text consumes, which affects both serving cost and the shape of the model's input. itself — the component that decides how many tokens a piece of text actually contains — was treated, implicitly, as invariant. Anthropic's update breaks the treaty. The rate has held; the denominator has moved. The hit is uneven, per independent breakdowns circulating among developer communities: English prose moves by roughly five percent, multilingual and CJK workloads run at the upper end of the 1.35x range, and structured data sits higher still. Early user reports from r/ClaudeAI and adjacent forums put real-world context-window inflation above fifty percent on some production traffic, outside even the range Anthropic disclosed.
The headline capability claim is coding. Per Anthropic's own benchmarks, 4.7 advances from 80.8 to 87.6 percent on SWE-bench Verified over 4.6, with the rigor-and-consistency language the lab reserves for versions it wants routed to the hardest work. A new `xhigh` effort level sits above `high` on the reasoning-latency dial. A `/ultrareview` command in Claude Code opens a dedicated second-pass review mode. Task budgets, in public beta, cap token spend per job rather than per call — acknowledging that the meaningful unit of agentic work is no longer the turn. Vision capabilities expand to accept images roughly three times larger than the prior ceiling, up to 3.75 megapixels.
The winners are the teams already standardised on 4.6 whose prompts and scaffolding translate without edit. The losers are the buyers whose unit economics were pegged against 4.6's tokenizerThe component of a language model that splits raw text into the smaller units (tokens) the model actually processes. Changes to a tokenizer shift how many tokens a given piece of text consumes, which affects both serving cost and the shape of the model's input. — per-seat coding assistants, customer-service automators, any operation that writes its margins to four decimals. A prompt unchanged in text now counts up to 35 percent more against the same bill on Anthropic's own numbers, and higher on independent ones. That is not a price hike in the strict sense, but the invoice outcome is identical. Whether the delta lands as margin compression or a quietly passed-through surcharge will depend on how tightly individual contracts specified the encoding.
What the release opens is the possibility that tokenizerThe component of a language model that splits raw text into the smaller units (tokens) the model actually processes. Changes to a tokenizer shift how many tokens a given piece of text consumes, which affects both serving cost and the shape of the model's input. revisions become the industry's preferred mechanism for absorbing training-cost inflation — invisible at the price card, fully visible at the invoice. What it forecloses is the assumption, held since the first public price cut in early 2024, that the frontier rate is a clean number with clean semantics. On the safety line, 4.7 carries a profile Anthropic describes as similar to 4.6 — low on deception and sycophancy, incrementally better on honesty and prompt-injectionAn attack that inserts hidden instructions into inputs an AI model processes — often via user-supplied text or retrieved documents — to override the model's intended behavior. A model's resistance to prompt injection is a common safety benchmark. resistance — with its cyber capabilities explicitly placed below those of Mythos Preview, the security-focused sibling announced earlier this month. The lineup is now segmented on purpose.