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

Claude Opus 4.8 vs Kimi K3

Claude Opus 4.8 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 4.8 when coding depth matter most; choose Kimi K3 when lower cost, lower latency is the priority.

Choose Claude Opus 4.8 if you…

  • Previous flagship still capable
  • Well-proven stability
  • 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 4.8 Higher overall
Anthropic · #5 overall
Overall81
Coding87
Multimodal89
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 4.8Kimi K3Verdict
VendorAnthropic (US)Moonshot AI (CN)Different vendors
Released2026.052026.06Kimi K3 is newer
Overall (rank)81 · #577 · #8Claude Opus 4.8 +4
Coding87 · #471 · #11Claude Opus 4.8 +16
Multimodal89 · #681 · #10Claude Opus 4.8 +8
Context window1M256KClaude Opus 4.8 larger
Max output128K64KKimi K3 longer
Effective-context9699Kimi K3 more reliable
Input $/1M$5$3Kimi K3 cheaper
Output $/1M$25$15Kimi K3 cheaper
Cache discount90% offcustomClaude Opus 4.8 deeper
Speed~40 tok/s~45 tok/sKimi K3 faster
TTFT1.0s0.7sKimi K3 snappier
Function calling8880Claude Opus 4.8 ahead
Refusal rate~9%~6%Kimi K3 less restrictive
English9475Claude Opus 4.8
Chinese7892Kimi 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 4.8 leads the overall aggregate by 4 points (81 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 clear gap: Claude Opus 4.8 scores 87 against 71. On multi-file edits, SWE-style tickets and long-horizon agent loops Claude Opus 4.8 needs fewer correction turns; Kimi K3 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Claude Opus 4.8 leads multimodal 89 vs 81. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Kimi K3 is faster in interactive use: ~45 tok/s with 0.7s TTFT versus ~40 tok/s with 1.0s TTFT (about 1.1× 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 4.8 and 256K for Kimi K3. Crucially, the larger nominal window does not win on usable recall: Claude Opus 4.8 advertises 1M but Kimi K3 scores higher on effective-context (99 vs 96), 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 4.8 at $5/$25 per 1M input/output tokens — list input is about 1.7× lower. Cache discounts (Claude Opus 4.8 90%) shift the effective bill, worked out below.

Chinese vs English

English: Claude Opus 4.8 94 vs Kimi K3 75. Chinese: 78 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 4.8 88 vs Kimi K3 80, so Claude Opus 4.8 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 4.8Kimi 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 4.8 (coding 87 vs 71).
IF you serve real-time users and latency is a product KPI  →  choose Kimi K3 (~45 tok/s, 0.7s TTFT).
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 96).
07

Frequently asked questions

Which is better for agentic coding?
Claude Opus 4.8 is decisively stronger for coding (87 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 4.8 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 4.8 (1M) and Kimi K3 (256K), but usable recall follows the effective-context score (96 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 4.8 is the global model (Chinese 78, 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 4.8 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 4.8 for Deep reasoning, Long documents; choose Kimi K3 for Long-document reading, Codebase analysis.