Llama 4 Maverick 17B 128E vs Kimi K2 Thinking

At a Glance

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Kimi K2 ThinkingMoonshot AI
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.60Openrouter · Sep 23, 2026
Output priceFrom · USD / 1M tokensNot reported$2.50Openrouter · Sep 23, 2026
Context windowMaximum documented tokens1,000K262K
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-4-Maverick-17B-128EKimi-K2-Thinking
DeveloperMetaMoonshot AI
FamilyLlama 4 Maverick 17b 128eKimi K2 Thinking
ModelLlama-4-Maverick-17B-128EKimi-K2-Thinking
VersionLlama-4-Maverick-17B-128EKimi-K2-Thinking
Lifecycleactiveactive
Released2025-04-052025-11-06
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText
Output modalitiesTextText
Context window1,000K262K
Total parameters401.6B1T
Active parameters17B32B
Licenseotherother
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownHugging Face (Standard), Openrouter (Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, tools

Llama 4 Maverick 17B 128E Capabilities

chatgenerationtools
Serving providers0
Canonical IDmeta-llama/Llama-4-Maverick-17B-128E

Kimi K2 Thinking Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDmoonshotai/Kimi-K2-Thinking

Primary Evidence

Sources and Freshness

Questions

Llama 4 Maverick 17B 128E vs Kimi K2 Thinking FAQs

Is Llama 4 Maverick 17B 128E or Kimi K2 Thinking better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E and Kimi K2 Thinking, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 4 Maverick 17B 128E 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 4 Maverick 17B 128E or Kimi K2 Thinking?+

Llama 4 Maverick 17B 128E has the larger sourced context window. Llama 4 Maverick 17B 128E supports 1,000K and Kimi K2 Thinking supports 262K.

Which performs better in benchmarks, Llama 4 Maverick 17B 128E 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 4 Maverick 17B 128E or Kimi K2 Thinking be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 4 Maverick 17B 128E is open weight; Kimi K2 Thinking is open weight.

Can Llama 4 Maverick 17B 128E and Kimi K2 Thinking understand images?+

Llama 4 Maverick 17B 128E is 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 4 Maverick 17B 128E or Kimi K2 Thinking?+

Neither has a larger sourced maximum output. Llama 4 Maverick 17B 128E is — and Kimi K2 Thinking is 131K.

Do Llama 4 Maverick 17B 128E and Kimi K2 Thinking support reasoning and tool use?+

Llama 4 Maverick 17B 128E: tool calling and image input. Kimi K2 Thinking: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 4 Maverick 17B 128E or Kimi K2 Thinking?+

Llama 4 Maverick 17B 128E has 0 sourced provider routes; Kimi K2 Thinking has 2, so Kimi K2 Thinking has broader tracked availability.

Which offers better value, Llama 4 Maverick 17B 128E 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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