Kimi K3.1 Pro vs Claude Opus 5.5: Two and a Half Points for Double the Money — and Seven Rows Out of Nine

Claude Opus 5.5 is the best model in the field on the independent index at 57.62. Kimi K3.1 Pro is 2.53 points behind at 55.09, at half the blended price — $8.00 per million against $4.00 — with an identical $0.20 cache-read rate and the same 1M context [INDEPENDENT, index v4.3.2, 26 October]. The OrcaRouter card for Kimi K3.1 Pro carries its rates and rows, both passed through at list with no markup; the comparison looks like the cleanest value argument in the market, and it is — for some workloads.

For others it is misleading, because Opus wins seven of the nine component rows below and two of its wins are enormous. The composite compresses a 17-point analysis gap and a 17-point knowledge gap into 2.5 points of index. If you buy on the composite, you are buying an average that may not describe your job.

The full table

Two wins for Kimi, seven for Opus, one tie. The composite’s 2.53-point gap is an average over a set of rows that range from a 7.6-point Kimi win to a 17.5-point Opus win.

What the 2.5 points hide

The knowledge gap is 16.9 points. On closed-book factual work, Opus answers 66.21% correctly against 51.02%, with a lower rate of confident errors. If your prompts are short and the answers come from the model rather than from you, this is the row that governs, and it is nearly seven times the composite’s gap.

The analysis gap is 17.5 points. On open-ended agentic analysis, Opus is in the top tier at 56.25; Kimi is at the floor at 38.75. A team that reads “2.5 points behind” and buys Kimi for an analyst agent will be disappointed by a margin the composite did not advertise.

The speed gap is 1.67×. Opus streams at 96.2 tok/s against Kimi’s 57.5. For interactive work that is the difference between a responsive assistant and a thoughtful one.

What the 2.5 points do describe is general capability on a ten-evaluation basket, and there Kimi’s two wins are real: the best terminal-agent score in the field and better long-context recall. Those are the rows where the half-price model is genuinely the better buy.

When half the price is the whole decision

Agentic terminal and coding work. Kimi wins the row that matters by 7.6 points at half the money. There is no argument for Opus here unless the analysis row is what your agent is actually doing.

Long-document processing at scale. Recall is a tie-plus (1.66 points), context is identical, cache read is identical, and the input line is half. A pipeline that reads a million tokens per document and asks for extraction should use Kimi.

High-volume execution with a supplied plan. If a planner upstream decides what to do and the model executes, Kimi’s weak analysis row never appears and its strong execution rows do.

When double the money is the cheap option

Knowledge-heavy work with short prompts. A 16.9-point omniscience gap is worth double on any task where a wrong fact costs more than a token.

Open-ended analytical assignments. Seventeen and a half points on AnalystAgent. If the model has to decide what to investigate, Opus is in a different class.

Interactive products. 96 tok/s is not a luxury; it is a product decision.

Vision work. MMMU-Pro 87.69 against 83.87 — a modest gap, in the same direction as everything else Opus leads.

The cost-per-point arithmetic

Both models are efficient in absolute terms; the comparison is closer than the price suggests.

Kimi returns 1.9 times more index points per blended dollar. If all you are buying is the composite, that settles it. If you are buying the knowledge or analysis rows, no efficiency metric applies, because you are not buying the average.

The 1.67× speed difference in queue terms

Speed rarely decides a benchmark and often decides a product. Opus 5.5 streams at 96.2 output tokens per second against Kimi K3.1 Pro’s 57.5, a factor of 1.67. For a 2,000-token answer that is 21 seconds against 35 — the difference between an assistant that holds a reader’s attention and one that does not.

At scale the same factor changes capacity rather than feel. A service that must sustain 500 concurrent streams needs 1.67 times the model-side concurrency on the slower model to hold the same queue depth, and if the slower model’s provider caps concurrency, the difference appears as a longer wait before the first token rather than as a visibly slower answer. Our own p50 on Kimi is 6.75 seconds across a seven-day window, and it drifted 13% upward over three days as traffic grew.

The practical translation: if a human is waiting, price the speed gap as a product cost rather than a benchmark row. If the work is batch, ignore it and price tokens.

How to decide in ten minutes

Write down, for your top two workloads, whether the answer lives in the prompt or in the weights, and whether the model executes a plan or makes one.

• In the prompt and executing → Kimi K3.1 Pro. Half price, better terminal and recall, identical cache and context.
• In the weights, or making the plan → Claude Opus 5.5. Double price, and the rows you need are 15 to 17 points ahead.
• Both → route. The classification is visible in the request, and on one key the policy is a few lines of code.

The one thing not to do is buy on the composite. A 2.5-point average over a row set that spans 17 points in each direction is the least informative number in this article, and it is the one on the headline. Two and a half points is a real difference between two models; it is not a description of what either of them will do on your work.

Sourcing note: all index, component, speed, context and rate-card figures are Artificial Analysis on index v4.3.2, read 26 October 2026, both models at maximum effort. Cache-read prices and blended prices are computed from the published cards. Our route serves Kimi K3.1 Pro at Moonshot’s list price with no markup; Opus 5.5’s card is Anthropic’s. The per-point arithmetic, decision rules and routing suggestion are ours.