Claude Opus 5 vs Llama 3.1 8B
At a Glance
| Compare | Claude Opus 5Anthropic |  Llama 3.1 8BMeta |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #3 of 4694.1 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #39 of 44$0.371 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #13 of 3857.6 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,000K | 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 Opus 5 | Llama-3.1-8B |
|---|---|---|
| Developer | Anthropic | Meta |
| Family | Claude 5 | Llama 3 1 8b |
| Model | Claude Opus 5 | Llama-3.1-8B |
| Version | Claude Opus 5 | Llama-3.1-8B |
| Lifecycle | active | active |
| Released | 2026-07-24 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 131K |
| Total parameters | Unknown | 8B |
| 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), Openrouter (Standard) | Hugging Face (Standard) |
| Capabilities | chat, generation, reasoning, tools | generation |
Claude Opus 5 Capabilities
Llama 3.1 8B Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Opus 5 vs Llama 3.1 8B FAQs
Is Claude Opus 5 or Llama 3.1 8B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 5 and Llama 3.1 8B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 5 or Llama 3.1 8B?+
Only Claude Opus 5 has a directly sourced input price: $5.00 per million tokens. Only Claude Opus 5 has a directly sourced output price: $25.00 per million tokens.
Which has a larger context window, Claude Opus 5 or Llama 3.1 8B?+
Claude Opus 5 has the larger sourced context window. Claude Opus 5 supports 1,000K and Llama 3.1 8B supports 131K.
Which performs better in benchmarks, Claude Opus 5 or Llama 3.1 8B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Opus 5 or Llama 3.1 8B be self-hosted?+
Llama 3.1 8B is the only model in this pair currently marked as self-hostable. Claude Opus 5 is not marked open weight; Llama 3.1 8B is open weight.
Can Claude Opus 5 and Llama 3.1 8B understand images?+
Claude Opus 5 is documented with image input; Llama 3.1 8B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 5 or Llama 3.1 8B?+
Neither has a larger sourced maximum output. Claude Opus 5 is 128K and Llama 3.1 8B is —.
Do Claude Opus 5 and Llama 3.1 8B support reasoning and tool use?+
Claude Opus 5: reasoning, tool calling, and image input. Llama 3.1 8B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 5 or Llama 3.1 8B?+
Claude Opus 5 has 3 sourced provider routes; Llama 3.1 8B has 1, so Claude Opus 5 has broader tracked availability.
Which offers better value, Claude Opus 5 or Llama 3.1 8B?+
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.