Head-to-head
Kimi K3 vs Claude Opus 4.7: which AI model wins in 2026?
Kimi K3 ($15/1M out) and Claude Opus 4.7 ($25/1M out) are two of the most-used AI models in 2026. Across 6 community votes, Kimi K3 leads with 57% approval.
Quick verdict
On Reasoning, Kimi K3 and Claude Opus 4.7 are tied at 4.5/5. On budget, Kimi K3 wins: it starts at $15/1M out versus $25/1M out for Claude Opus 4.7.
Line-by-line comparison
Strengths and weaknesses
Kimi K3
- #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
- $3/$15 per 1M tokens (input drops to $0.30 on cache hit), flat across the full 1M context: still well under US closed-frontier pricing, with an OpenAI-compatible API and OpenRouter availability
- Native multimodal input (text, image, video) and open weights announced for 2026-07-27 under an expected Modified-MIT-style license, positioning it as the first 3T-class open-weights frontier model
- 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%)
- Political censorship on sensitive topics in Mandarin (deflects on Xi, CCP, Tiananmen) and Beijing jurisdiction for API data, a compliance blocker for many EU and enterprise deployments; UK AISI/CAISI also found its cyber guardrails failed to block exploit-development attempts during testing
- 'Open weights' is theoretical for most: ~594 GB in native MXFP4 requiring a multi-GPU datacenter to self-host, and the weights (plus final license) were still unpublished at review time
Claude Opus 4.7
- 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
- Fine cost control via new xhigh effort level and Task Budgets (beta): low-effort 4.7 roughly matches medium-effort 4.6 output quality
- Recent knowledge: reliable cutoff of January 2026, the freshest of any Claude model at release
- 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
- Moderate latency with minutes-long turns at high effort; fast mode is a premium research preview already deprecated on 4.7
- Superseded by Opus 4.8 at the same $5/$25 within ~3 months, and real-time cybersecurity safeguards can false-positive on legitimate security work
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on Kimi K3
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.
The arena’s verdict on Claude Opus 4.7
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).
What the crowd says
On Kimi K3
“Tried it for our knowledge-base assistant and the hallucination rate is real: it confidently invented two API endpoints in one afternoon. 33 tokens per second on top of that. Back to waiting for the open weights to fine-tune.”
“Our Zapier-style automation stack runs on it now. Tool orchestration that GPT-5.5 fumbled works reliably, and the cache-hit pricing makes repeated workflows genuinely cheap.”
“The Frontend Arena ranking is deserved. Gave it the same dashboard spec I gave Fable 5 and K3's React came out cleaner, with fewer invented props. At $3/$15 through OpenRouter it was a drop-in swap.”
On Claude Opus 4.7
“Watch your invoices. New tokenizer counts ~30% more tokens for the same text, and it narrates every tiny step. Sticker price unchanged, effective cost definitely not.”
“Came from 4.6 and stopped chunking repos entirely. 1M context, 128K output, flat $5/$25 with no long-context premium. That pricing decision alone won me over.”
“87.6 SWE-bench Verified is not just marketing, it closes tickets GPT-5.4 fumbles. And the hi-res vision with pixel-accurate coords finally makes screenshot debugging useful.”
Keep comparing
Frequently asked questions
Is Kimi K3 better than Claude Opus 4.7?
The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, Kimi K3 or Claude Opus 4.7?
Kimi K3 is cheaper: it starts at $15/1M out, while Claude Opus 4.7 starts at $25/1M out.
How much do Kimi K3 and Claude Opus 4.7 cost per 1M tokens?
Kimi K3: $3/1M in per 1M input tokens, $15/1M out per 1M output tokens. Claude Opus 4.7: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.