Llama 3.1 70B vs Kimi K3

At a Glance

Compare
Kimi K3Moonshot AI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#17 of 4670.3 score · 3/3 sources · complete
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#30 of 44$0.194 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#21 of 3852.5 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$2.85Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$14.25Deepinfra · Sep 22, 2026
Context windowMaximum documented tokens131K1,049K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 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

FieldLlama-3.1-70BKimi-K3
DeveloperMetaMoonshot AI
FamilyLlama 3 1 70bKimi K3
ModelLlama-3.1-70BKimi-K3
VersionLlama-3.1-70BKimi-K3
Lifecycleactiveactive
Released2024-07-232026-07-16
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image
Output modalitiesTextText
Context window131K1,049K
Total parameters70.6B2.8T
Active parametersUnknown104B
Licensellama3.1other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessHugging Face (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesgenerationchat, generation, reasoning

Llama 3.1 70B Capabilities

generation
Serving providers1
Canonical IDmeta-llama/Llama-3.1-70B

Kimi K3 Capabilities

chatgenerationreasoning
Serving providers5
Canonical IDmoonshotai/Kimi-K3

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 70B vs Kimi K3 FAQs

Is Llama 3.1 70B or Kimi K3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 70B and Kimi K3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 70B or Kimi K3?+

Only Kimi K3 has a directly sourced input price: $2.85 per million tokens. Only Kimi K3 has a directly sourced output price: $14.25 per million tokens.

Which has a larger context window, Llama 3.1 70B or Kimi K3?+

Kimi K3 has the larger sourced context window. Llama 3.1 70B supports 131K and Kimi K3 supports 1,049K.

Which performs better in benchmarks, Llama 3.1 70B or Kimi K3?+

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

Can Llama 3.1 70B or Kimi K3 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 70B is open weight; Kimi K3 is open weight.

Can Llama 3.1 70B and Kimi K3 understand images?+

Llama 3.1 70B is not documented with image input; Kimi K3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 70B or Kimi K3?+

Neither has a larger sourced maximum output. Llama 3.1 70B is — and Kimi K3 is —.

Do Llama 3.1 70B and Kimi K3 support reasoning and tool use?+

Llama 3.1 70B: none of these features are definitively sourced. Kimi K3: reasoning and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 70B or Kimi K3?+

Llama 3.1 70B has 1 sourced provider route; Kimi K3 has 5, so Kimi K3 has broader tracked availability.

Which offers better value, Llama 3.1 70B 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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