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

Kimi K3 vs Gemini 3.5 Flash

Kimi K3 wins on Overall, Coding; Gemini 3.5 Flash wins on Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorMoonshot AI / Google
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Kimi K3 when coding depth, long-context reliability, lower refusal matter most; choose Gemini 3.5 Flash when lower cost, lower latency, fine-tuning/ecosystem is the priority.

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

Choose Gemini 3.5 Flash if you…

  • Extremely fast
  • Low price
  • Native multimodal
  • Built for scale
  • Best for: Fast tasks, Multimodal, Low cost
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.

Kimi K3 Higher overall
Moonshot AI · #8 overall
Overall77
Coding71
Multimodal81
VS
Gemini 3.5 Flash
Google · #14 overall
Overall69
Coding64
Multimodal86

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.

DimensionKimi K3Gemini 3.5 FlashVerdict
VendorMoonshot AI (CN)Google (US)Different vendors
Released2026.062026.07Gemini 3.5 Flash is newer
Overall (rank)77 · #869 · #14Kimi K3 +8
Coding71 · #1164 · #17Kimi K3 +7
Multimodal81 · #1086 · #7Gemini 3.5 Flash +5
Context window256K1MGemini 3.5 Flash larger
Max output64K128KKimi K3 longer
Effective-context9988Kimi K3 more reliable
Input $/1M$3$1.5Gemini 3.5 Flash cheaper
Output $/1M$15$9Gemini 3.5 Flash cheaper
Cache discountcustomnoneGemini 3.5 Flash deeper
Speed~45 tok/s~80 tok/sGemini 3.5 Flash faster
TTFT0.7s0.3sGemini 3.5 Flash snappier
Function calling8075Kimi K3 ahead
Refusal rate~6%~10%Kimi K3 less restrictive
English7585Gemini 3.5 Flash
Chinese9272Kimi K3
Modalitiestext, imagetext, image, audio, videodifferent coverage
Open weightsYesNoKimi K3 is open
Fine-tuningNoYes
Free tierKimi App free; free quota on the open platformGemini App free; API free tier
SOC2 / no-trainno / yesyes / yes
Private deploymentYesNoKimi 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

Kimi K3 leads the overall aggregate by 8 points (77 vs 69). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Gemini 3.5 Flash remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: Kimi K3 scores 71 against 64. On multi-file edits, SWE-style tickets and long-horizon agent loops Kimi K3 needs fewer correction turns; Gemini 3.5 Flash is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.5 Flash leads multimodal 86 vs 81. A concrete modality difference: Gemini 3.5 Flash additionally handles audio, video.

Speed & latency

Gemini 3.5 Flash is faster in interactive use: ~80 tok/s with 0.3s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.8× 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 256K for Kimi K3 and 1M for Gemini 3.5 Flash. Crucially, the larger nominal window does not win on usable recall: Gemini 3.5 Flash advertises 1M but Kimi K3 scores higher on effective-context (99 vs 88), i.e. it actually retains more of what it was given.

Price & total cost

Gemini 3.5 Flash is the cheaper API at $1.5/$9 versus Kimi K3 at $3/$15 per 1M input/output tokens — list input is about 2.0× lower.

Chinese vs English

English: Kimi K3 75 vs Gemini 3.5 Flash 85. Chinese: 92 vs 72. 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: Kimi K3 80 vs Gemini 3.5 Flash 75, so Kimi K3 has the edge on structured tool use. Fine-tuning is available from Gemini 3.5 Flash. 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 monthKimi K3Gemini 3.5 FlashGap
List priceno cache applied$750100M in × $3  +  30M out × $15$420100M in × $1.5  +  30M out × $91.79×gap
With caching90% of inputs cache-hit$750no published cache discount$420no published cache discount1.79×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 Kimi K3 (coding 71 vs 64).
IF you serve real-time users and latency is a product KPI  →  choose Gemini 3.5 Flash (~80 tok/s, 0.3s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Gemini 3.5 Flash ($$1.5/$$9 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Kimi K3 (effective context 99 vs 88).
07

Frequently asked questions

Is Kimi K3 worth the higher price over Gemini 3.5 Flash?
At list the input rate is 2.0x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Kimi K3's stronger dimensions protect revenue; for routine volume Gemini 3.5 Flash is the economical pick.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Kimi K3 is about $750/month and Gemini 3.5 Flash about $420/month after cache discounts ($750 and $420 at list).
Does the bigger context window actually matter?
Nominal windows are Kimi K3 (256K) and Gemini 3.5 Flash (1M), but usable recall follows the effective-context score (99 vs 88). 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 Gemini 3.5 Flash is the global model (Chinese 72, 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; Gemini 3.5 Flash 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?
Gemini 3.5 Flash 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.