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

Qwen3.8-Max vs Kimi K3

Qwen3.8-Max 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.

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

Verdict at a glance

Bottom line: Choose Qwen3.8-Max when coding depth matter most; choose Kimi K3 when its stronger dimensions is the priority.

Choose Qwen3.8-Max if you…

  • Top-tier Chinese
  • Balanced multimodal
  • Alibaba Cloud ecosystem
  • Good value
  • Best for: Chinese tasks, Multimodal, 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.

Qwen3.8-Max Higher overall
Alibaba · #4 overall
Overall82
Coding77
Multimodal92
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.

DimensionQwen3.8-MaxKimi K3Verdict
VendorAlibaba (CN)Moonshot AI (CN)Different vendors
Released2026.072026.06Qwen3.8-Max is newer
Overall (rank)82 · #477 · #8Qwen3.8-Max +5
Coding77 · #671 · #11Qwen3.8-Max +6
Multimodal92 · #281 · #10Qwen3.8-Max +11
Context window1M256KQwen3.8-Max larger
Max output128K64KKimi K3 longer
Effective-context9599Kimi K3 more reliable
Input $/1M$2.5$3Qwen3.8-Max cheaper
Output $/1M$7.5$15Qwen3.8-Max cheaper
Cache discount80% offcustomQwen3.8-Max deeper
Speed~55 tok/s~45 tok/sQwen3.8-Max faster
TTFT0.6s0.7sQwen3.8-Max snappier
Function calling8880Qwen3.8-Max ahead
Refusal rate~10%~6%Kimi K3 less restrictive
English7875Qwen3.8-Max
Chinese9892Qwen3.8-Max
Modalitiestext, imagetext, imageSame
Open weightsNoYesKimi K3 is open
Fine-tuningYesNo
Free tierFree credits for new Alibaba Cloud Bailiang usersKimi App free; free quota on the open platform
SOC2 / no-trainno / yesno / yes
Private deploymentYesYesBoth support it

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

Qwen3.8-Max leads the overall aggregate by 5 points (82 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 modest gap: Qwen3.8-Max scores 77 against 71. On multi-file edits, SWE-style tickets and long-horizon agent loops Qwen3.8-Max needs fewer correction turns; Kimi K3 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Qwen3.8-Max leads multimodal 92 vs 81. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Qwen3.8-Max is faster in interactive use: ~55 tok/s with 0.6s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.2× 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 Qwen3.8-Max and 256K for Kimi K3. Crucially, the larger nominal window does not win on usable recall: Qwen3.8-Max advertises 1M but Kimi K3 scores higher on effective-context (99 vs 95), i.e. it actually retains more of what it was given.

Price & total cost

Qwen3.8-Max is the cheaper API at $2.5/$7.5 versus Kimi K3 at $3/$15 per 1M input/output tokens — list input is about 1.2× lower. Cache discounts (Qwen3.8-Max 80%) shift the effective bill, worked out below.

Chinese vs English

English: Qwen3.8-Max 78 vs Kimi K3 75. Chinese: 98 vs 92. For Chinese-language production, Qwen3.8-Max is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Qwen3.8-Max 88 vs Kimi K3 80, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Qwen3.8-Max. 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 monthQwen3.8-MaxKimi K3Gap
List priceno cache applied$475100M in × $2.5  +  30M out × $7.5$750100M in × $3  +  30M out × $151.58×gap
With caching90% of inputs cache-hit$29590M cached in × $0.5  +  10M in × $2.5  +  30M out × $7.5$750no published cache discount2.54×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 Qwen3.8-Max (coding 77 vs 71).
IF you serve real-time users and latency is a product KPI  →  choose Qwen3.8-Max (~55 tok/s, 0.6s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Qwen3.8-Max ($$2.5/$$7.5 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Kimi K3 (effective context 99 vs 95).
07

Frequently asked questions

How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Qwen3.8-Max is about $295/month and Kimi K3 about $750/month after cache discounts ($475 and $750 at list).
Does the bigger context window actually matter?
Nominal windows are Qwen3.8-Max (1M) and Kimi K3 (256K), but usable recall follows the effective-context score (95 vs 99). Prefer the higher effective-context model for long-document work where nothing can be missed.
Can I self-host either model?
Kimi K3 ships open weights and can be self-hosted (GPU permitting) for data control; Qwen3.8-Max is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.
Which one supports fine-tuning?
Qwen3.8-Max supports fine-tuning; Kimi K3 does not at this tier. If you plan to adapt the model to a narrow domain, that is a concrete differentiator.
What is the single-line recommendation?
Choose Qwen3.8-Max for Chinese tasks, Multimodal; choose Kimi K3 for Long-document reading, Codebase analysis.
How quickly do these rankings change?
Modelspectra refreshes the aggregate as new public benchmarks and prices appear. Treat scores within 3 points as a tie and re-check before a committed purchase.