Kimi K2.7 Code
● ExcellentMoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...
Specifications
| Attribute | Value |
|---|---|
| Lab | Moonshotai |
| Tags | Coding Intelligent |
| Overall Score | 8.5/10 |
| Release Date | 2026-06 |
| Context Window | 262,144 tokens |
| Input Price / 1M | $0.95 |
| Output Price / 1M | $4.00 |
| Input Modalities | Text, Image, Video |
| Output Modalities | Text |
Strengths
Weaknesses
Best For
In Depth: Kimi K2.7 Code
Draft layout · copy TKBenchmark Performance
[Lead paragraph — ~60 words. Anchor Kimi K2.7 Code's overall score of 8.5/10 against the headline benchmarks it actually competes on (MMLU, HumanEval, MATH, GSM8K, LMSYS Arena). Name the closest peers above and below it on this leaderboard so the reader instantly understands the tier.]
[Detail paragraph — ~80 words. Walk through 2–3 specific benchmark numbers with citations: e.g. "scores X on MMLU vs. Y for the next-best model in its class," "Arena ELO of Z places it between A and B." Mention where the model over- or under-performs its overall rank — a 9.0 model that's a 9.5 on coding but a 8.2 on math is the kind of nuance that wins long-tail queries like "Kimi K2.7 Code coding benchmark" or "Kimi K2.7 Code vs [peer]".]
Pricing & Value
[Lead paragraph — ~50 words. State the input/output prices in plain English ("$0.95 in, $4.00 out per million tokens") and convert to something tangible — cost of a 10k-token analysis, cost of a 1M-token agentic run, cost vs. the cheapest model on the leaderboard.]
[Detail paragraph — ~90 words. Compare Kimi K2.7 Code's price-per-point-of-score against 2–3 named peers. Call out whether you're paying for raw intelligence, long context (262,144 tokens here), low latency, or brand premium. Flag any tier discounts, batch pricing, or caching that materially change the effective cost. If this model is overpriced for its score, say so plainly — that honesty is what ranks.]
Who Should Use This
[Lead paragraph — ~50 words. One sentence per persona: the developer building X, the analyst doing Y, the team that already runs Z. Each should resolve to a concrete decision: "pick Kimi K2.7 Code if…" and "skip it if…".]
- [Persona 1 — e.g. "Backend engineers wiring up production agents." One sentence on why this model fits, one on the tradeoff they accept.]
- [Persona 2 — e.g. "Solo founders prototyping fast." Same structure: why it fits, what they give up.]
- [Persona 3 — e.g. "Enterprise teams that need a long-context workhorse." Why it fits, the constraint.]
- [Anti-persona — "Skip Kimi K2.7 Code if you're optimizing for X or Y." Be specific; this is the most-cited line in a review.]
Release & Version History
[Lead paragraph — ~50 words. Anchor the 2026-06 release in context: what it replaced inside Moonshotai's lineup, what the lab claimed it improved, and how those claims held up against independent benchmarks in the weeks after launch.]
[Detail paragraph — ~90 words. Walk the version trail: previous generation, this model, any planned successor or sibling (mini/flash/opus tier). Note pricing or context-window changes vs. the predecessor. Mention deprecation timelines if the lab has announced any — readers searching "Kimi K2.7 Code deprecated" or "Kimi K2.7 Code successor" land directly here. Close with the editorial verdict: is this the version to standardize on for the next 6 months, or a stopgap?]
Sources & Further Reading
Related Models
Scores are aggregated from public benchmarks (MMLU, HumanEval, MATH, GSM8K, LMSYS) and normalized to a 1–10 scale. Methodology →