Claude Haiku 4.5 vs Pixtral Large
At a Glance
| Compare | Claude Haiku 4.5Anthropic |  Pixtral LargeMistral 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 | Not reported |
| Output priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 200K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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 | Pixtral Large |
|---|---|---|
| Developer | Anthropic | Mistral AI |
| Family | Claude 4 5 | Pixtral Large |
| Model | Claude Haiku 4.5 | Pixtral Large |
| Version | Claude Haiku 4.5 | Pixtral Large |
| Lifecycle | active | deprecated |
| Released | 2025-10-15 | 2024-11-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Document |
| Output modalities | Text | Text |
| Context window | 200K | 131K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | No |
| Self-hostable | No | No |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation, structured_outputs, tools, vision |
Claude Haiku 4.5 Capabilities
Pixtral Large Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Haiku 4.5 vs Pixtral Large FAQs
Is Claude Haiku 4.5 or Pixtral Large better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Haiku 4.5 and Pixtral Large, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Haiku 4.5 or Pixtral Large?+
Only Claude Haiku 4.5 has a directly sourced input price: $1.00 per million tokens. Only Claude Haiku 4.5 has a directly sourced output price: $5.00 per million tokens.
Which has a larger context window, Claude Haiku 4.5 or Pixtral Large?+
Claude Haiku 4.5 has the larger sourced context window. Claude Haiku 4.5 supports 200K and Pixtral Large supports 131K.
Which performs better in benchmarks, Claude Haiku 4.5 or Pixtral Large?+
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 Pixtral Large be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Haiku 4.5 is not marked open weight; Pixtral Large is not marked open weight.
Can Claude Haiku 4.5 and Pixtral Large understand images?+
Claude Haiku 4.5 is documented with image input; Pixtral Large is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Haiku 4.5 or Pixtral Large?+
Neither has a larger sourced maximum output. Claude Haiku 4.5 is 64K and Pixtral Large is —.
Do Claude Haiku 4.5 and Pixtral Large support reasoning and tool use?+
Claude Haiku 4.5: reasoning, tool calling, and image input. Pixtral Large: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Haiku 4.5 or Pixtral Large?+
Claude Haiku 4.5 has 2 sourced provider routes; Pixtral Large has 0, so Claude Haiku 4.5 has broader tracked availability.
Which offers better value, Claude Haiku 4.5 or Pixtral Large?+
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.