Claude Mythos 5.1 vs Llama 3.1 8B Instruct
At a Glance
| Compare | Claude Mythos 5.1Anthropic | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $10.00Anthropic ↗ · Sep 2, 2026 | $0.050Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | $50.00Anthropic ↗ · Sep 2, 2026 | $0.080Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 1,000K | 131K |
| Model facts checked | Sep 2, 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 Mythos 5.1 | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | Anthropic | Meta |
| Family | Claude 5 1 | Llama 3 1 8b Instruct |
| Model | Claude Mythos 5.1 | Llama-3.1-8B-Instruct |
| Version | Claude Mythos 5.1 | Llama-3.1-8B-Instruct |
| Lifecycle | active | active |
| Released | 2026-09-01 | 2024-07-23 |
| Knowledge cutoff | 2026-06-01 | 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 (Project Glasswing) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Claude Mythos 5.1 Capabilities
Llama 3.1 8B Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Mythos 5.1 vs Llama 3.1 8B Instruct FAQs
Is Claude Mythos 5.1 or Llama 3.1 8B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Mythos 5.1 and Llama 3.1 8B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Mythos 5.1 or Llama 3.1 8B Instruct?+
Claude Mythos 5.1 is $10.00 and Llama 3.1 8B Instruct is $0.050 per million tokens, so Llama 3.1 8B Instruct is cheaper on this metric. Claude Mythos 5.1 is $50.00 and Llama 3.1 8B Instruct is $0.080 per million tokens, so Llama 3.1 8B Instruct is cheaper on this metric.
Which has a larger context window, Claude Mythos 5.1 or Llama 3.1 8B Instruct?+
Claude Mythos 5.1 has the larger sourced context window. Claude Mythos 5.1 supports 1,000K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, Claude Mythos 5.1 or Llama 3.1 8B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Mythos 5.1 or Llama 3.1 8B Instruct be self-hosted?+
Llama 3.1 8B Instruct is the only model in this pair currently marked as self-hostable. Claude Mythos 5.1 is not marked open weight; Llama 3.1 8B Instruct is open weight.
Can Claude Mythos 5.1 and Llama 3.1 8B Instruct understand images?+
Claude Mythos 5.1 is documented with image input; Llama 3.1 8B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Mythos 5.1 or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. Claude Mythos 5.1 is 128K and Llama 3.1 8B Instruct is —.
Do Claude Mythos 5.1 and Llama 3.1 8B Instruct support reasoning and tool use?+
Claude Mythos 5.1: reasoning, tool calling, and image input. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Mythos 5.1 or Llama 3.1 8B Instruct?+
Claude Mythos 5.1 has 1 sourced provider route; Llama 3.1 8B Instruct has 2, so Llama 3.1 8B Instruct has broader tracked availability.
Which offers better value, Claude Mythos 5.1 or Llama 3.1 8B 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.