Gemini 3.1 Pro vs Kimi K3

Market Position

LiveBench Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIDeepSeekZ.aiMiniMaxOpenAIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed current model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.020$0.050$0.100$0.200$0.5006873788287Grok Build 0.1GLM 5.3 FlashGPT-5.6 Luna (max)DeepSeek V4 Flash Vision ExpGPT-5.6 Sol (max)Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated current-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

This current-market view appears only when both compared models are reviewed current models with publisher-reported LiveBench token accounting and current sourced USD output rates.

Benchmark Performance

Available Benchmarks

BenchmarkGemini 3.1 ProKimi-K3
LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · statistical tie1,480.10100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1476.89767236, 1483.30025658]; votes 102763; rank 111,476.31100% of row best · rating · kimi-k3-max; 95% CI [1470.86166728, 1481.76254962]; votes 17895; rank 16
LiveBench2026-06-25 · overall · leader81.66100% of row best · percent · gemini-3.1-pro-preview-high · 13,380 output tokens / case81.0299% of row best · percent · kimi-k3 · 13,647 output tokens / case
ToneBench2026-08-28-10-task-cd9819ab6e4d · overall_score · leader79.8991% of row best · points · Gemini 3.1 Pro (high thinking) · 10,009 output tokens / case88.19100% of row best · points · Kimi K3 · 15,023 output tokens / case
Overall ResultCounted from the protocol-matched rows above · 1 tie1 benchmark winTied overall1 benchmark winTied overall

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

FieldAt a Glance
Google DeepMind · previewGemini 3.1 ProVerified Aug 29, 2026
Moonshot AI · activeKimi K3Verified Aug 28, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldGemini 3.1 ProKimi-K3
DeveloperGoogle DeepMindMoonshot AI
FamilyGemini 3Kimi K3
ModelGemini 3.1 ProKimi-K3
VersionGemini 3.1 ProKimi-K3
Lifecyclepreviewactive
ReleasedUnknown2026-07-16
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText, Image
Output modalitiesTextText
Context window1,049K1,049K
Total parametersUnknown2.8T
Active parametersUnknown104B
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessGoogle AI (Standard), Google Gemini (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning

13 comparable fields · 10 material differences · Pair passes the primary-source comparison gate

Gemini 3.1 Pro Capabilities

chatgenerationreasoningtools
Input price$2.00
Output price$12.00
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.1-pro-preview

Kimi K3 Capabilities

chatgenerationreasoning
Input price$2.85
Output price$14.25
Serving providers5
Canonical IDmoonshotai/Kimi-K3

Internal Comparison Graph

Related Comparisons

All text comparisons →
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Primary Evidence

Sources and Freshness

Questions

Gemini 3.1 Pro vs Kimi K3 FAQs

Is Gemini 3.1 Pro or Kimi K3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Kimi K3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.1 Pro or Kimi K3?+

Gemini 3.1 Pro is $2.00 and Kimi K3 is $2.85 per million tokens, so Gemini 3.1 Pro is cheaper on this metric. Gemini 3.1 Pro is $12.00 and Kimi K3 is $14.25 per million tokens, so Gemini 3.1 Pro is cheaper on this metric.

Which has a larger context window, Gemini 3.1 Pro or Kimi K3?+

Neither model has a larger sourced context window in this comparison. Gemini 3.1 Pro is 1,049K and Kimi K3 is 1,049K.

Which performs better in benchmarks, Gemini 3.1 Pro or Kimi K3?+

There is no overall benchmark winner: The verified common benchmarks do not produce a majority winner.

Can Gemini 3.1 Pro or Kimi K3 be self-hosted?+

Kimi K3 is the only model in this pair currently marked as self-hostable. Gemini 3.1 Pro is not marked open weight; Kimi K3 is open weight.

Can Gemini 3.1 Pro and Kimi K3 understand images?+

Gemini 3.1 Pro is documented with image input; Kimi K3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.1 Pro or Kimi K3?+

Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Kimi K3 is —.

Do Gemini 3.1 Pro and Kimi K3 support reasoning and tool use?+

Gemini 3.1 Pro: reasoning, tool calling, and image input. Kimi K3: reasoning and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.1 Pro or Kimi K3?+

Gemini 3.1 Pro has 2 sourced provider routes; Kimi K3 has 5, so Kimi K3 has broader tracked availability.

Which offers better value, Gemini 3.1 Pro or Kimi K3?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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