# Kimi K3 vs Claude Opus 4.7 (2026): side-by-side comparison

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/vs/kimi-k3-vs-claude-opus-4-7
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Kimi K3 | Claude Opus 4.7 |
|---|---|---|
| Price | $15/1M out | $25/1M out |
| Crowd score | 57% (3 votes) | 57% (3 votes) |
| provider | Moonshot AI | Anthropic |
| contextWindow | 1M tokens | 1M tokens (128K max output) |
| priceIn | $3/1M in | $5/1M in |
| priceOut | $15/1M out | $25/1M out |
| modalities | text, vision, video input | text + image input (up to 2576px), text output |
| openWeights | no | no |
| reasoning (1-5) | 4.5 | 4.5 |
| coding (1-5) | 4.5 | 4.5 |
| writing (1-5) | 4 | 4.5 |
| speed (1-5) | 2 | 2.5 |
| valueForMoney (1-5) | 3.5 | 3 |

### Kimi K3

> Moonshot's 2.8T-parameter MoE that sparked the 'new DeepSeek moment': frontier-adjacent scores at $3/$15

Strengths:
- #3 on the Artificial Analysis Intelligence Index (57) at launch, comparable to Claude Opus 4.8 and GPT-5.5: the closest a Chinese lab has come to the closed US frontier
- 93.4% on SWE-bench Verified in Vals AI's independent harness (GPT-5.6 Sol: 96.2%, Claude Fable 5: 95.0%) and #1 on Arena.ai's Frontend Code Arena at 1679 points, ahead of Fable 5
- 93.5% on GPQA Diamond (between Fable 5's 92.6% and GPT-5.6's 94.1%), 96.1% on AIME 2025, and Elo 1668 on GDPval-AA v2 agentic work, second only to Fable 5
- Strong agentic profile: #1 on AutomationBench-AA SaaS workflows (53%), long-horizon terminal and repo navigation via Kimi Code, and ~21% fewer output tokens than K2 for more intelligence

Weaknesses:
- 3x price jump over Kimi K2.6 ($0.95/$4 to $3/$15) makes it the most expensive Chinese model ever shipped, at Claude Sonnet 5 list price: the '10x cheaper than US models' era is over (Simon Willison documented the hike)
- Slow: 33 output tokens/s, ranked #145 of 190 models on Artificial Analysis, a poor fit for interactive use
- Hallucination rate climbed from 39% (K2.6) to 51% on AA-Omniscience as the model now attempts answers it would previously refuse (Fable 5 sits at a comparable 54.9%)

Verdict: Kimi K3 is the strongest argument yet that the frontier is no longer exclusively American: #3 on Artificial Analysis, top-tier GPQA and SWE-bench Verified scores, and the best frontend-code arena ranking in the business, at roughly half to a third of US closed-frontier prices. Take it for agentic coding, frontend work and SaaS automation where its benchmarks are strongest, or if your roadmap depends on self-hosting a frontier-class model once the weights land. Skip it for interactive products (33 tokens/s is slow), for anything touching politically sensitive content or strict EU data-residency requirements (Mandarin-language censorship, Beijing jurisdiction), and for high-accuracy retrieval where its 51% hallucination rate on AA-Omniscience demands a verification layer. Cost-obsessed teams should note DeepSeek V4 still delivers vastly more tokens per dollar; K3's pitch is peak capability per dollar, not cheapest tokens.

Full review: https://glad-ia-tor.com/tool/kimi-k3 · Markdown: https://glad-ia-tor.com/tool/kimi-k3.md

### Claude Opus 4.7

> Anthropic's April 2026 Opus: 87.6% SWE-bench Verified, 1M context, high-res vision, now behind Opus 4.8

Strengths:
- 87.6% SWE-bench Verified (up from 80.8% on Opus 4.6) and 64.3% SWE-bench Pro at launch, ahead of GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%)
- 1M-token context window and 128K max output at flat $5/$25 pricing with no long-context premium (300K output via Batch API beta)
- First Claude with high-resolution vision: accepts images up to 2576px on the long edge with pixel-accurate coordinates, ~3x prior detail
- Standout code review: finds more real bugs with stronger cross-file reasoning than rivals in independent tests, and 21% fewer document-reasoning errors than Opus 4.6

Weaknesses:
- New tokenizer inflates token counts roughly 30% for the same text versus pre-4.7 models (per Anthropic's own docs), raising effective per-request cost despite the unchanged sticker price
- Very verbose in agentic use: one benchmark found GPT-5.5 used 72% fewer output tokens on equivalent coding tasks, and reviewers call its narration over-communicative
- Breaking API changes bite migrators: temperature/top_p/top_k and thinking budget_tokens now return 400 errors, and thinking text is hidden by default

Verdict: Choose Opus 4.7 only if you are already pinned to it for reproducibility: Opus 4.8 costs the same $5/$25, keeps an identical API surface, and outperforms it, making it the better default for new projects. It remains a very strong pick for agentic coding, code review and 1M-context document work, and is a clear upgrade over Opus 4.6. Teams migrating from 4.6 should budget for breaking API changes and a tokenizer that yields roughly 30% more tokens per prompt. Cost-sensitive users should look at Sonnet 5, which delivers near-Opus quality at $3/$15 (intro $2/$10 through August 31, 2026).

Full review: https://glad-ia-tor.com/tool/claude-opus-4-7 · Markdown: https://glad-ia-tor.com/tool/claude-opus-4-7.md

## More

Full llm-models ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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