DeepSeek V4.1 Flash vs Llama 3.3 70B Instruct
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek | |
|---|---|---|
| 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 | $0.10Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.60Deepinfra ↗ · Sep 22, 2026 | $0.32Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 131K |
| Model facts checked | Sep 10, 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 | DeepSeek-V4.1-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| Developer | DeepSeek | Meta |
| Family | Deepseek V4 1 | Llama 3 3 70b Instruct |
| Model | DeepSeek-V4.1-Flash | Llama-3.3-70B-Instruct |
| Version | DeepSeek-V4.1-Flash | Llama-3.3-70B-Instruct |
| Lifecycle | active | active |
| Released | 2026-09-10 | 2024-12-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 131K |
| Total parameters | 763.2B | 70.6B |
| Active parameters | Unknown | Unknown |
| License | mit | llama3.3 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | chat, generation, tools |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| Transformer layers | 40 layers | Unknown |
DeepSeek V4.1 Flash Capabilities
Llama 3.3 70B Instruct Capabilities
Related Comparisons
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|---|---|---|---|
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 Llama 3.3 70B Instruct FAQs
Is DeepSeek V4.1 Flash or Llama 3.3 70B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and Llama 3.3 70B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?+
DeepSeek V4.1 Flash is $0.15 and Llama 3.3 70B Instruct is $0.10 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric. DeepSeek V4.1 Flash is $0.60 and Llama 3.3 70B Instruct is $0.32 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric.
Which has a larger context window, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?+
DeepSeek V4.1 Flash has the larger sourced context window. DeepSeek V4.1 Flash supports 1,049K and Llama 3.3 70B Instruct supports 131K.
Which performs better in benchmarks, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?+
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 Llama 3.3 70B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4.1 Flash is open weight; Llama 3.3 70B Instruct is open weight.
Can DeepSeek V4.1 Flash and Llama 3.3 70B Instruct understand images?+
DeepSeek V4.1 Flash is documented with image input; Llama 3.3 70B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?+
Neither has a larger sourced maximum output. DeepSeek V4.1 Flash is 393K and Llama 3.3 70B Instruct is —.
Do DeepSeek V4.1 Flash and Llama 3.3 70B Instruct support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Llama 3.3 70B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or Llama 3.3 70B Instruct?+
DeepSeek V4.1 Flash has 3 sourced provider routes; Llama 3.3 70B Instruct has 3, a tie.
Which offers better value, DeepSeek V4.1 Flash or Llama 3.3 70B 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.