Head-to-head

Kimi K3 logovsClaude Opus 4.5 logo

Kimi K3 vs Claude Opus 4.5: which AI model wins in 2026?

Kimi K3 ($15/1M out) and Claude Opus 4.5 ($25/1M out) are two of the most-used AI models in 2026. Across 3 community votes, Kimi K3 leads with 57% approval.

Quick verdict

On Reasoning, pick Kimi K3: the arena rates it 4.5/5 against 3.5/5 for Claude Opus 4.5. On budget, Kimi K3 wins: it starts at $15/1M out versus $25/1M out for Claude Opus 4.5.

Line-by-line comparison

From
$15/1M outOfficial Moonshot API list price for kimi-k3: $3/1M input (cache miss), $0.30/1M on cache hit, $15/1M output including reasoning trace, flat across the 1M context (no length tiers). Same $3/$15 on OpenRouter (no cache pricing exposed there). A 3x increase over Kimi K2.6's $0.95/$4. Verified against platform.kimi.ai/docs/pricing/chat-k3, 2026-07.
$25/1M outOfficial Anthropic API list price for claude-opus-4-5 (single tier, no long-context premium; 200K context, 64K max output); same $5/$25 rate as its Opus 4.6-4.8 successors; 50% batch discount and prompt caching apply. Verified against platform.claude.com models overview 2026-07.
Provider
Moonshot AI
Anthropic
Context window
1M tokens
200K tokens
Input price
$3/1M in
$5/1M in
Output price
$15/1M out
$25/1M out
Modalities
text, vision, video input
text, vision
Open weights
No
No
Crowd score
57%(3)
50%(0)
Arena ratings (1-5)
Reasoning
4.5
3.5
Coding
4.5
3.5
Writing
4.0
3.5
Speed
2.0
2.5
Value
3.5
2.5

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 4.5

  • First model past 80% on SWE-bench Verified (80.9% at launch), beating Gemini 3 Pro and GPT-5.1 on real-world coding
  • 66% price cut vs Opus 4.1 ($5/$25 vs $15/$75 per 1M tokens) made Opus-tier viable for production workloads
  • 48-76% fewer output tokens than Sonnet 4.5 at matched or better quality, compounding the price cut
  • Effort parameter (introduced with this model) lets devs trade reasoning depth for cost and latency per call
  • Strong hands-on reports: one-shot complex refactors, caught race conditions other models missed, converged in ~4 agentic iterations vs ~10 for rivals
  • +29% on Vending-Bench vs Sonnet 4.5, with fewer dead-ends on long-horizon autonomous tasks
  • 200K context window only (64K max output), far behind the 1M of Gemini 3 Pro and later Claude models; users reported selective attention above ~70% context fill
  • Gated to $100-200/month Max tiers in Claude apps at launch; Pro subscribers were locked out and heavy users still hit limits (HN called it 'penny-wise and pound-foolish')
  • Moderate latency; extended thinking adds cost and delay on simple tasks
  • Superseded since early 2026: Opus 4.6/4.7/4.8 cost the same $5/$25 with 1M context and higher benchmarks, leaving 4.5 no price advantage
  • Legacy API surface: manual budget_tokens extended thinking rather than the adaptive thinking of newer Claude models

Cast your verdict

One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.

Kimi K3$15/1M out
57%crowd score · 3
Claude Opus 4.5$25/1M out
50%crowd score · 0

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 4.5

Pick Claude Opus 4.5 only if you have a workload already tuned and pinned to this snapshot (claude-opus-4-5-20251101) and need stability. It was a landmark release, the first past 80% on SWE-bench Verified and a 66% price cut over Opus 4.1, but Anthropic now sells Opus 4.6 through 4.8 at the identical $5/$25 rate with a 1M context window and better scores. Anyone starting a new project should choose Opus 4.8 instead, and cost-sensitive users get near-Opus coding from Sonnet 5 at $3/$15 (intro $2/$10 through Aug 2026). Avoid it entirely if your prompts approach the 200K context ceiling.

What the crowd says

On Kimi K3

Captain Churn

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.

Golden Thumbicus

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.

Saint Deployus

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 4.5

No verdicts yet. Be the first to speak.

Frequently asked questions

Is Kimi K3 better than Claude Opus 4.5?

The crowd currently sides with Kimi K3: 57% recommend it, versus 50% for Claude Opus 4.5 (3 votes). On Reasoning, Kimi K3 rates higher (4.5/5 vs 3.5/5). 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 4.5?

Kimi K3 is cheaper: it starts at $15/1M out, while Claude Opus 4.5 starts at $25/1M out.

How much do Kimi K3 and Claude Opus 4.5 cost per 1M tokens?

Kimi K3: $3/1M in per 1M input tokens, $15/1M out per 1M output tokens. Claude Opus 4.5: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.