Claude Haiku 4.5 vs Llama 3.1 405B
At a Glance
| Compare | Claude Haiku 4.5Anthropic |  Llama 3.1 405BMeta |
|---|---|---|
| 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 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 | Llama-3.1-405B |
|---|---|---|
| Developer | Anthropic | Meta |
| Family | Claude 4 5 | Llama 3 1 405b |
| Model | Claude Haiku 4.5 | Llama-3.1-405B |
| Version | Claude Haiku 4.5 | Llama-3.1-405B |
| Lifecycle | active | active |
| Released | 2025-10-15 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 200K | 131K |
| Total parameters | Unknown | 405.9B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.1 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | generation |
Claude Haiku 4.5 Capabilities
Llama 3.1 405B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Haiku 4.5 vs Llama 3.1 405B FAQs
Is Claude Haiku 4.5 or Llama 3.1 405B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Haiku 4.5 and Llama 3.1 405B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Haiku 4.5 or Llama 3.1 405B?+
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 Llama 3.1 405B?+
Claude Haiku 4.5 has the larger sourced context window. Claude Haiku 4.5 supports 200K and Llama 3.1 405B supports 131K.
Which performs better in benchmarks, Claude Haiku 4.5 or Llama 3.1 405B?+
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 Llama 3.1 405B be self-hosted?+
Llama 3.1 405B is the only model in this pair currently marked as self-hostable. Claude Haiku 4.5 is not marked open weight; Llama 3.1 405B is open weight.
Can Claude Haiku 4.5 and Llama 3.1 405B understand images?+
Claude Haiku 4.5 is documented with image input; Llama 3.1 405B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Haiku 4.5 or Llama 3.1 405B?+
Neither has a larger sourced maximum output. Claude Haiku 4.5 is 64K and Llama 3.1 405B is —.
Do Claude Haiku 4.5 and Llama 3.1 405B support reasoning and tool use?+
Claude Haiku 4.5: reasoning, tool calling, and image input. Llama 3.1 405B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Haiku 4.5 or Llama 3.1 405B?+
Claude Haiku 4.5 has 2 sourced provider routes; Llama 3.1 405B has 1, so Claude Haiku 4.5 has broader tracked availability.
Which offers better value, Claude Haiku 4.5 or Llama 3.1 405B?+
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.