Llama 3.1 405B Instruct vs Kimi K2 Thinking

At a Glance

Compare
Kimi K2 ThinkingMoonshot AI
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.60Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$2.50Openrouter · Sep 22, 2026
Context windowMaximum documented tokens131K262K
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-3.1-405B-InstructKimi-K2-Thinking
DeveloperMetaMoonshot AI
FamilyLlama 3 1 405b InstructKimi K2 Thinking
ModelLlama-3.1-405B-InstructKimi-K2-Thinking
VersionLlama-3.1-405B-InstructKimi-K2-Thinking
Lifecycleactiveactive
Released2024-07-232025-11-06
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window131K262K
Total parameters405.9B1T
Active parametersUnknown32B
Licensellama3.1other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Hugging Face (Standard), Openrouter (Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, tools

Llama 3.1 405B Instruct Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-3.1-405B-Instruct

Kimi K2 Thinking Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDmoonshotai/Kimi-K2-Thinking

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 405B Instruct vs Kimi K2 Thinking FAQs

Is Llama 3.1 405B Instruct or Kimi K2 Thinking better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B Instruct and Kimi K2 Thinking, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

Only Kimi K2 Thinking has a directly sourced input price: $0.60 per million tokens. Only Kimi K2 Thinking has a directly sourced output price: $2.50 per million tokens.

Which has a larger context window, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

Kimi K2 Thinking has the larger sourced context window. Llama 3.1 405B Instruct supports 131K and Kimi K2 Thinking supports 262K.

Which performs better in benchmarks, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

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

Can Llama 3.1 405B Instruct or Kimi K2 Thinking be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 405B Instruct is open weight; Kimi K2 Thinking is open weight.

Can Llama 3.1 405B Instruct and Kimi K2 Thinking understand images?+

Llama 3.1 405B Instruct is not documented with image input; Kimi K2 Thinking is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

Neither has a larger sourced maximum output. Llama 3.1 405B Instruct is — and Kimi K2 Thinking is 131K.

Do Llama 3.1 405B Instruct and Kimi K2 Thinking support reasoning and tool use?+

Llama 3.1 405B Instruct: tool calling. Kimi K2 Thinking: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

Llama 3.1 405B Instruct has 1 sourced provider route; Kimi K2 Thinking has 2, so Kimi K2 Thinking has broader tracked availability.

Which offers better value, Llama 3.1 405B Instruct or Kimi K2 Thinking?+

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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