DeepSeek V4.1 Flash vs Jev
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  JevTypeSafe |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #5 of 44$0.022 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.15DeepSeek ↗ · Sep 10, 2026 | $0.042TypeSafe ↗ · Sep 17, 2026 |
| Output priceFrom · USD / 1M tokens | $0.60Deepinfra ↗ · Sep 22, 2026 | $0.000TypeSafe ↗ · Sep 17, 2026 |
| Context windowMaximum documented tokens | 1,049K | 64K |
| Model facts checked | Sep 10, 2026View model evidence → | Sep 17, 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 | DeepSeek-V4.1-Flash | Jev |
|---|---|---|
| Developer | DeepSeek | TypeSafe |
| Family | Deepseek V4 1 | Jev |
| Model | DeepSeek-V4.1-Flash | Jev |
| Version | DeepSeek-V4.1-Flash | Jev |
| Lifecycle | active | active |
| Released | 2026-09-10 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Model-specific input |
| Output modalities | Text | Model-specific input |
| Context window | 1,049K | 64K |
| Total parameters | 763.2B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | TypeSafe (Standard) |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Maximum Choice cardinality | Unknown | 255 options |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Default request rate limit | Unknown | 1200 requests per minute |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
| Transformer layers | 40 layers | Unknown |
DeepSeek V4.1 Flash Capabilities
Jev Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4.1 Flash vs Jev FAQs
Is DeepSeek V4.1 Flash or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or Jev?+
DeepSeek V4.1 Flash is $0.15 and Jev is $0.042 per million tokens, so Jev is cheaper on this metric. DeepSeek V4.1 Flash is $0.60 and Jev is $0.000 per million tokens, so Jev is cheaper on this metric.
Which has a larger context window, DeepSeek V4.1 Flash or Jev?+
DeepSeek V4.1 Flash has the larger sourced context window. DeepSeek V4.1 Flash supports 1,049K and Jev supports 64K.
Which performs better in benchmarks, DeepSeek V4.1 Flash or Jev?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4.1 Flash or Jev be self-hosted?+
DeepSeek V4.1 Flash is the only model in this pair currently marked as self-hostable. DeepSeek V4.1 Flash is open weight; Jev is not marked open weight.
Can DeepSeek V4.1 Flash and Jev understand images?+
DeepSeek V4.1 Flash is documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or Jev?+
Neither has a larger sourced maximum output. DeepSeek V4.1 Flash is 393K and Jev is —.
Do DeepSeek V4.1 Flash and Jev support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or Jev?+
DeepSeek V4.1 Flash has 3 sourced provider routes; Jev has 1, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash or Jev?+
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.