Kimi K3 vs Grok 4.7

At a Glance

Compare
Kimi K3Moonshot AI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#17 of 4670.3 score · 3/3 sources · completeUnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#30 of 44$0.194 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#21 of 3852.5 score · 3/3 sources · completeUnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$2.85Deepinfra · Sep 23, 2026$2.00Xai · Sep 22, 2026
Output priceFrom · USD / 1M tokens$14.25Deepinfra · Sep 23, 2026$6.00Xai · Sep 22, 2026
Context windowMaximum documented tokens1,049K500K
Model facts checkedAug 28, 2026View model evidence →Sep 22, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldKimi-K3Grok 4.7
DeveloperMoonshot AIxAI
FamilyKimi K3Grok 4
ModelKimi-K3Grok 4.7
VersionKimi-K3Grok 4.7
Lifecycleactiveactive
Released2026-07-162026-09-21
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,049K500K
Total parameters2.8TUnknown
Active parameters104BUnknown
LicenseotherUnknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessDeepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Xai (Standard)
Capabilitieschat, generation, reasoningchat, generation, reasoning, tools

Kimi K3 Capabilities

chatgenerationreasoning
Serving providers5
Canonical IDmoonshotai/Kimi-K3

Grok 4.7 Capabilities

chatgenerationreasoningtools
Serving providers1
Canonical IDxai/grok-4.7

Primary Evidence

Sources and Freshness

Questions

Kimi K3 vs Grok 4.7 FAQs

Is Kimi K3 or Grok 4.7 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K3 and Grok 4.7, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Kimi K3 or Grok 4.7?+

Kimi K3 is $2.85 and Grok 4.7 is $2.00 per million tokens, so Grok 4.7 is cheaper on this metric. Kimi K3 is $14.25 and Grok 4.7 is $6.00 per million tokens, so Grok 4.7 is cheaper on this metric.

Which has a larger context window, Kimi K3 or Grok 4.7?+

Kimi K3 has the larger sourced context window. Kimi K3 supports 1,049K and Grok 4.7 supports 500K.

Which performs better in benchmarks, Kimi K3 or Grok 4.7?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Kimi K3 or Grok 4.7 be self-hosted?+

Kimi K3 is the only model in this pair currently marked as self-hostable. Kimi K3 is open weight; Grok 4.7 is not marked open weight.

Can Kimi K3 and Grok 4.7 understand images?+

Kimi K3 is documented with image input; Grok 4.7 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Kimi K3 or Grok 4.7?+

Neither has a larger sourced maximum output. Kimi K3 is — and Grok 4.7 is —.

Do Kimi K3 and Grok 4.7 support reasoning and tool use?+

Kimi K3: reasoning and image input. Grok 4.7: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Kimi K3 or Grok 4.7?+

Kimi K3 has 5 sourced provider routes; Grok 4.7 has 1, so Kimi K3 has broader tracked availability.

Which offers better value, Kimi K3 or Grok 4.7?+

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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