Llama 3.1 8B vs Kimi K2 Thinking
At a Glance
| Compare |  Llama 3.1 8BMeta |  Kimi K2 ThinkingMoonshot AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.60Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $2.50Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 262K |
| Model facts checked | Aug 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
Side-by-Side Facts
| Field | Llama-3.1-8B | Kimi-K2-Thinking |
|---|---|---|
| Developer | Meta | Moonshot AI |
| Family | Llama 3 1 8b | Kimi K2 Thinking |
| Model | Llama-3.1-8B | Kimi-K2-Thinking |
| Version | Llama-3.1-8B | Kimi-K2-Thinking |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2025-11-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 262K |
| Total parameters | 8B | 1T |
| Active parameters | Unknown | 32B |
| License | llama3.1 | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
Llama 3.1 8B Capabilities
Kimi K2 Thinking Capabilities
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Llama-3.1-8BMeta | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Llama-3.1-8BMeta | vs | Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Llama-3.1-8BMeta | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 8B vs Kimi K2 Thinking FAQs
Is Llama 3.1 8B or Kimi K2 Thinking better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B 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 8B 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 8B or Kimi K2 Thinking?+
Kimi K2 Thinking has the larger sourced context window. Llama 3.1 8B supports 131K and Kimi K2 Thinking supports 262K.
Which performs better in benchmarks, Llama 3.1 8B 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 8B or Kimi K2 Thinking be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 8B is open weight; Kimi K2 Thinking is open weight.
Can Llama 3.1 8B and Kimi K2 Thinking understand images?+
Llama 3.1 8B 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 8B or Kimi K2 Thinking?+
Neither has a larger sourced maximum output. Llama 3.1 8B is — and Kimi K2 Thinking is 131K.
Do Llama 3.1 8B and Kimi K2 Thinking support reasoning and tool use?+
Llama 3.1 8B: none of these features are definitively sourced. Kimi K2 Thinking: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 8B or Kimi K2 Thinking?+
Llama 3.1 8B 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 8B 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.