Claude Haiku 4.5 vs Kimi K2 Instruct
At a Glance
| Compare | Claude Haiku 4.5Anthropic |  Kimi K2 InstructMoonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #43 of 469.0 score · 2/3 sources · provisional · missing LiveBench · full-core range 6.0–39.3 | UnrankedNot in the 46-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.00Anthropic ↗ · Sep 3, 2026 | $0.57Openrouter ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $2.30Openrouter ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 200K | 131K |
| 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 | Claude Haiku 4.5 | Kimi-K2-Instruct |
|---|---|---|
| Developer | Anthropic | Moonshot AI |
| Family | Claude 4 5 | Kimi K2 Instruct |
| Model | Claude Haiku 4.5 | Kimi-K2-Instruct |
| Version | Claude Haiku 4.5 | Kimi-K2-Instruct |
| Lifecycle | active | active |
| Released | 2025-10-15 | 2025-07-11 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 200K | 131K |
| Total parameters | Unknown | 1T |
| Active parameters | Unknown | 32B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Claude Haiku 4.5 Capabilities
Kimi K2 Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Haiku 4.5 vs Kimi K2 Instruct FAQs
Is Claude Haiku 4.5 or Kimi K2 Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Haiku 4.5 and Kimi K2 Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Haiku 4.5 or Kimi K2 Instruct?+
Claude Haiku 4.5 is $1.00 and Kimi K2 Instruct is $0.57 per million tokens, so Kimi K2 Instruct is cheaper on this metric. Claude Haiku 4.5 is $5.00 and Kimi K2 Instruct is $2.30 per million tokens, so Kimi K2 Instruct is cheaper on this metric.
Which has a larger context window, Claude Haiku 4.5 or Kimi K2 Instruct?+
Claude Haiku 4.5 has the larger sourced context window. Claude Haiku 4.5 supports 200K and Kimi K2 Instruct supports 131K.
Which performs better in benchmarks, Claude Haiku 4.5 or Kimi K2 Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Haiku 4.5 or Kimi K2 Instruct be self-hosted?+
Kimi K2 Instruct is the only model in this pair currently marked as self-hostable. Claude Haiku 4.5 is not marked open weight; Kimi K2 Instruct is open weight.
Can Claude Haiku 4.5 and Kimi K2 Instruct understand images?+
Claude Haiku 4.5 is documented with image input; Kimi K2 Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Haiku 4.5 or Kimi K2 Instruct?+
Neither has a larger sourced maximum output. Claude Haiku 4.5 is 64K and Kimi K2 Instruct is —.
Do Claude Haiku 4.5 and Kimi K2 Instruct support reasoning and tool use?+
Claude Haiku 4.5: reasoning, tool calling, and image input. Kimi K2 Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Haiku 4.5 or Kimi K2 Instruct?+
Claude Haiku 4.5 has 2 sourced provider routes; Kimi K2 Instruct has 2, a tie.
Which offers better value, Claude Haiku 4.5 or Kimi K2 Instruct?+
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.