Llama 3.1 405B vs Kimi K2.7 Code

At a Glance

Compare
Kimi K2.7 CodeMoonshot AI
Intelligence, Cost, and Efficiency
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#9 of 44$0.042 per LiveBench case
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.68Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$3.21Openrouter · Sep 22, 2026
Context windowMaximum documented tokens131K262K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 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-3.1-405BKimi K2.7 Code
DeveloperMetaMoonshot AI
FamilyLlama 3 1 405bKimi K2 7
ModelLlama-3.1-405BKimi K2.7 Code
VersionLlama-3.1-405BKimi K2.7 Code
Lifecycleactiveactive
Released2024-07-232026-06-11
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video
Output modalitiesTextText
Context window131K262K
Total parameters405.9B1T
Active parametersUnknown32B
Licensellama3.1modified-mit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesgenerationagents, chat, coding, reasoning, tools, vision

Llama 3.1 405B Capabilities

generation
Serving providers1
Canonical IDmeta-llama/Llama-3.1-405B

Kimi K2.7 Code Capabilities

agentschatcodingreasoningtoolsvision
Serving providers4
Canonical IDmoonshotai/Kimi-K2.7-Code

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 405B vs Kimi K2.7 Code FAQs

Is Llama 3.1 405B or Kimi K2.7 Code better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B and Kimi K2.7 Code, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 405B or Kimi K2.7 Code?+

Only Kimi K2.7 Code has a directly sourced input price: $0.68 per million tokens. Only Kimi K2.7 Code has a directly sourced output price: $3.21 per million tokens.

Which has a larger context window, Llama 3.1 405B or Kimi K2.7 Code?+

Kimi K2.7 Code has the larger sourced context window. Llama 3.1 405B supports 131K and Kimi K2.7 Code supports 262K.

Which performs better in benchmarks, Llama 3.1 405B or Kimi K2.7 Code?+

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 or Kimi K2.7 Code be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 405B is open weight; Kimi K2.7 Code is open weight.

Can Llama 3.1 405B and Kimi K2.7 Code understand images?+

Llama 3.1 405B is not documented with image input; Kimi K2.7 Code is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 405B or Kimi K2.7 Code?+

Neither has a larger sourced maximum output. Llama 3.1 405B is — and Kimi K2.7 Code is —.

Do Llama 3.1 405B and Kimi K2.7 Code support reasoning and tool use?+

Llama 3.1 405B: none of these features are definitively sourced. Kimi K2.7 Code: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 405B or Kimi K2.7 Code?+

Llama 3.1 405B has 1 sourced provider route; Kimi K2.7 Code has 4, so Kimi K2.7 Code has broader tracked availability.

Which offers better value, Llama 3.1 405B or Kimi K2.7 Code?+

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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