Llama 3.1 70B Instruct vs Kimi K2 Thinking
At a Glance
| Compare |  Kimi K2 ThinkingMoonshot AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.40Openrouter ↗ · Sep 23, 2026 | $0.60Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | $0.40Openrouter ↗ · Sep 23, 2026 | $2.50Openrouter ↗ · Sep 23, 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-3.1-70B-Instruct | Kimi-K2-Thinking |
|---|---|---|
| Developer | Meta | Moonshot AI |
| Family | Llama 3 1 70b Instruct | Kimi K2 Thinking |
| Model | Llama-3.1-70B-Instruct | Kimi-K2-Thinking |
| Version | Llama-3.1-70B-Instruct | 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 | 70.6B | 1T |
| Active parameters | Unknown | 32B |
| License | llama3.1 | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Llama 3.1 70B Instruct Capabilities
Kimi K2 Thinking Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 3.1 70B Instruct vs Kimi K2 Thinking FAQs
Is Llama 3.1 70B Instruct or Kimi K2 Thinking better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 70B 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 70B Instruct or Kimi K2 Thinking?+
Llama 3.1 70B Instruct is $0.40 and Kimi K2 Thinking is $0.60 per million tokens, so Llama 3.1 70B Instruct is cheaper on this metric. Llama 3.1 70B Instruct is $0.40 and Kimi K2 Thinking is $2.50 per million tokens, so Llama 3.1 70B Instruct is cheaper on this metric.
Which has a larger context window, Llama 3.1 70B Instruct or Kimi K2 Thinking?+
Kimi K2 Thinking has the larger sourced context window. Llama 3.1 70B Instruct supports 131K and Kimi K2 Thinking supports 262K.
Which performs better in benchmarks, Llama 3.1 70B 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 70B Instruct or Kimi K2 Thinking be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 70B Instruct is open weight; Kimi K2 Thinking is open weight.
Can Llama 3.1 70B Instruct and Kimi K2 Thinking understand images?+
Llama 3.1 70B 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 70B Instruct or Kimi K2 Thinking?+
Neither has a larger sourced maximum output. Llama 3.1 70B Instruct is — and Kimi K2 Thinking is 131K.
Do Llama 3.1 70B Instruct and Kimi K2 Thinking support reasoning and tool use?+
Llama 3.1 70B 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 70B Instruct or Kimi K2 Thinking?+
Llama 3.1 70B 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 70B 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.