Claude Mythos 5.1 vs DeepSeek V4.1 Flash
At a Glance
| Compare | Claude Mythos 5.1Anthropic |  DeepSeek V4.1 FlashDeepSeek |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #5 of 44$0.022 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $10.00Anthropic ↗ · Sep 2, 2026 | $0.15DeepSeek ↗ · Sep 10, 2026 |
| Output priceFrom · USD / 1M tokens | $50.00Anthropic ↗ · Sep 2, 2026 | $0.60Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,000K | 1,049K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 10, 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 | DeepSeek-V4.1-Flash |
|---|---|---|
| Developer | Anthropic | DeepSeek |
| Family | Claude 5 1 | Deepseek V4 1 |
| Model | Claude Mythos 5.1 | DeepSeek-V4.1-Flash |
| Version | Claude Mythos 5.1 | DeepSeek-V4.1-Flash |
| Lifecycle | active | active |
| Released | 2026-09-01 | 2026-09-10 |
| Knowledge cutoff | 2026-06-01 | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 1,049K |
| Total parameters | Unknown | 763.2B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Project Glasswing) | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision |
| Architecture design | Unknown | Causal Encoder-Decoder (20 encoder + 20 decoder layers) |
| Backbone parameters | Unknown | 552000000000 parameters |
| Active parameters during decode | Unknown | 16000000000 parameters |
| Active parameters during prefill | Unknown | 8000000000 parameters |
| Pre-training corpus | Unknown | 45000000000000 tokens |
| Reasoning effort range | Unknown | 1–100 |
| Routed experts per MoE layer | Unknown | 384 experts |
| Routed experts per token | Unknown | 6 experts |
| Transformer layers | Unknown | 40 layers |
Claude Mythos 5.1 Capabilities
DeepSeek V4.1 Flash Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Claude Mythos 5.1Anthropic | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
Claude Mythos 5.1Anthropic | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Claude Mythos 5.1Anthropic | family variantsimage, text |
Claude Mythos 5.1Anthropic | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Claude Mythos 5.1Anthropic | vs | Grok 4.6xAI | cross-developer peersimage, text |
Claude Mythos 5.1Anthropic | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Claude Mythos 5.1Anthropic | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Mythos 5.1Anthropic | cross-developer peersimage, text |
Claude Mythos 5.1Anthropic | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Claude Mythos 5.1Anthropic | vs |  Hy4 previewTencent | cross-developer peerstext |
Claude Mythos 5.1Anthropic | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Claude Mythos 5.1Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Mythos 5.1 vs DeepSeek V4.1 Flash FAQs
Is Claude Mythos 5.1 or DeepSeek V4.1 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Mythos 5.1 and DeepSeek V4.1 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
Claude Mythos 5.1 is $10.00 and DeepSeek V4.1 Flash is $0.15 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric. Claude Mythos 5.1 is $50.00 and DeepSeek V4.1 Flash is $0.60 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric.
Which has a larger context window, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
DeepSeek V4.1 Flash has the larger sourced context window. Claude Mythos 5.1 supports 1,000K and DeepSeek V4.1 Flash supports 1,049K.
Which performs better in benchmarks, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
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 DeepSeek V4.1 Flash be self-hosted?+
DeepSeek V4.1 Flash is the only model in this pair currently marked as self-hostable. Claude Mythos 5.1 is not marked open weight; DeepSeek V4.1 Flash is open weight.
Can Claude Mythos 5.1 and DeepSeek V4.1 Flash understand images?+
Claude Mythos 5.1 is documented with image input; DeepSeek V4.1 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
DeepSeek V4.1 Flash has the larger sourced maximum output: Claude Mythos 5.1 supports 128K and DeepSeek V4.1 Flash supports 393K output tokens.
Do Claude Mythos 5.1 and DeepSeek V4.1 Flash support reasoning and tool use?+
Claude Mythos 5.1: reasoning, tool calling, and image input. DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
Claude Mythos 5.1 has 1 sourced provider route; DeepSeek V4.1 Flash has 3, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, Claude Mythos 5.1 or DeepSeek V4.1 Flash?+
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.