MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Claude Opus 5 vs Kimi K3

Claude Opus 5 wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorAnthropic / Moonshot AI
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Claude Opus 5 when coding depth matter most; choose Kimi K3 when lower cost is the priority.

Choose Claude Opus 5 if you…

  • Dependable and stable
  • Strong reasoning depth
  • Strong long-document handling
  • Best for: Deep reasoning, Long documents, Coding

Choose Kimi K3 if you…

  • Best-in-class long text
  • Open-source and self-hostable
  • Strong codebase understanding
  • Fair price
  • Best for: Long-document reading, Codebase analysis, Chinese
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Claude Opus 5 Higher overall
Anthropic · #2 overall
Overall87
Coding97
Multimodal91
VS
Kimi K3
Moonshot AI · #8 overall
Overall77
Coding71
Multimodal81

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionClaude Opus 5Kimi K3Verdict
VendorAnthropic (US)Moonshot AI (CN)Different vendors
Released2026.072026.06Claude Opus 5 is newer
Overall (rank)87 · #277 · #8Claude Opus 5 +10
Coding97 · #271 · #11Claude Opus 5 +26
Multimodal91 · #581 · #10Claude Opus 5 +10
Context window1M256KClaude Opus 5 larger
Max output128K64KKimi K3 longer
Effective-context9799Kimi K3 more reliable
Input $/1M$5$3Kimi K3 cheaper
Output $/1M$25$15Kimi K3 cheaper
Cache discount90% offcustomClaude Opus 5 deeper
Speed~45 tok/s~45 tok/sTie
TTFT0.9s0.7sKimi K3 snappier
Function calling9080Claude Opus 5 ahead
Refusal rate~7%~6%Kimi K3 less restrictive
English9675Claude Opus 5
Chinese8092Kimi K3
Modalitiestext, imagetext, imageSame
Open weightsNoYesKimi K3 is open
Fine-tuningNoNo
Free tierNo free tierKimi App free; free quota on the open platform
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesKimi K3

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Claude Opus 5 leads the overall aggregate by 10 points (87 vs 77). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Kimi K3 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a decisive gap: Claude Opus 5 scores 97 against 71. On multi-file edits, SWE-style tickets and long-horizon agent loops Claude Opus 5 needs fewer correction turns; Kimi K3 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Claude Opus 5 leads multimodal 91 vs 81. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Claude Opus 5 is faster in interactive use: ~45 tok/s with 0.9s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.0× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 1M for Claude Opus 5 and 256K for Kimi K3. Crucially, the larger nominal window does not win on usable recall: Claude Opus 5 advertises 1M but Kimi K3 scores higher on effective-context (99 vs 97), i.e. it actually retains more of what it was given.

Price & total cost

Kimi K3 is the cheaper API at $3/$15 versus Claude Opus 5 at $5/$25 per 1M input/output tokens — list input is about 1.7× lower. Cache discounts (Claude Opus 5 90%) shift the effective bill, worked out below.

Chinese vs English

English: Claude Opus 5 96 vs Kimi K3 75. Chinese: 80 vs 92. For Chinese-language production, Kimi K3 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Claude Opus 5 90 vs Kimi K3 80, so Claude Opus 5 has the edge on structured tool use. Neither offers standard fine-tuning. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthClaude Opus 5Kimi K3Gap
List priceno cache applied$1,250100M in × $5  +  30M out × $25$750100M in × $3  +  30M out × $151.67×gap
With caching90% of inputs cache-hit$84590M cached in × $0.5  +  10M in × $5  +  30M out × $25$750no published cache discount1.13×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Claude Opus 5 (coding 97 vs 71).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Kimi K3 ($$3/$$15 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Kimi K3 (effective context 99 vs 97).
IF the product is Chinese-first  →  choose Kimi K3 (Chinese 92 vs 80).
07

Frequently asked questions

Which is better for agentic coding?
Claude Opus 5 is decisively stronger for coding (97 vs 71 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; Kimi K3 is fine for routine scripts.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Claude Opus 5 is about $845/month and Kimi K3 about $750/month after cache discounts ($1,250 and $750 at list).
Does the bigger context window actually matter?
Nominal windows are Claude Opus 5 (1M) and Kimi K3 (256K), but usable recall follows the effective-context score (97 vs 99). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
Kimi K3 is the Chinese model (Chinese score 92, domestic cloud, possible private deployment) while Claude Opus 5 is the global model (Chinese 80, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
Kimi K3 ships open weights and can be self-hosted (GPU permitting) for data control; Claude Opus 5 is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.
What is the single-line recommendation?
Choose Claude Opus 5 for Deep reasoning, Long documents; choose Kimi K3 for Long-document reading, Codebase analysis.