Claude Mythos 5.1 vs Phi-4 Multimodal Instruct
At a Glance
| Compare | Claude Mythos 5.1Anthropic | Phi-4 Multimodal InstructMicrosoft |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $10.00Anthropic ↗ · Sep 2, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $50.00Anthropic ↗ · Sep 2, 2026 | Not reported |
| 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 | Phi-4-multimodal-instruct |
|---|---|---|
| Developer | Anthropic | Microsoft |
| Family | Claude 5 1 | Phi 4 Multimodal Instruct |
| Model | Claude Mythos 5.1 | Phi-4-multimodal-instruct |
| Version | Claude Mythos 5.1 | Phi-4-multimodal-instruct |
| Lifecycle | active | active |
| Released | 2026-09-01 | 2025-02-26 |
| Knowledge cutoff | 2026-06-01 | Unknown |
| Input modalities | Text, Image | Text, Image, Audio |
| Output modalities | Text | Text |
| Context window | 1,000K | 131K |
| Total parameters | Unknown | 5.6B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Unknown |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Project Glasswing) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation |
Claude Mythos 5.1 Capabilities
Phi-4 Multimodal Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Mythos 5.1 vs Phi-4 Multimodal Instruct FAQs
Is Claude Mythos 5.1 or Phi-4 Multimodal Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Mythos 5.1 and Phi-4 Multimodal 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 Phi-4 Multimodal Instruct?+
Only Claude Mythos 5.1 has a directly sourced input price: $10.00 per million tokens. Only Claude Mythos 5.1 has a directly sourced output price: $50.00 per million tokens.
Which has a larger context window, Claude Mythos 5.1 or Phi-4 Multimodal Instruct?+
Claude Mythos 5.1 has the larger sourced context window. Claude Mythos 5.1 supports 1,000K and Phi-4 Multimodal Instruct supports 131K.
Which performs better in benchmarks, Claude Mythos 5.1 or Phi-4 Multimodal 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 Phi-4 Multimodal Instruct be self-hosted?+
Phi-4 Multimodal Instruct is the only model in this pair currently marked as self-hostable. Claude Mythos 5.1 is not marked open weight; Phi-4 Multimodal Instruct is open weight.
Can Claude Mythos 5.1 and Phi-4 Multimodal Instruct understand images?+
Claude Mythos 5.1 is documented with image input; Phi-4 Multimodal Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Mythos 5.1 or Phi-4 Multimodal Instruct?+
Neither has a larger sourced maximum output. Claude Mythos 5.1 is 128K and Phi-4 Multimodal Instruct is —.
Do Claude Mythos 5.1 and Phi-4 Multimodal Instruct support reasoning and tool use?+
Claude Mythos 5.1: reasoning, tool calling, and image input. Phi-4 Multimodal Instruct: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Mythos 5.1 or Phi-4 Multimodal Instruct?+
Claude Mythos 5.1 has 1 sourced provider route; Phi-4 Multimodal Instruct has 0, so Claude Mythos 5.1 has broader tracked availability.
Which offers better value, Claude Mythos 5.1 or Phi-4 Multimodal 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.