DeepSeek V4 Flash Vision Exp vs Llama 3.1 70B
At a Glance
| Compare |  DeepSeek V4 Flash Vision ExpDeepSeek |  Llama 3.1 70BMeta |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.22DeepSeek ↗ · Sep 2, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.66DeepSeek ↗ · Sep 2, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 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 | DeepSeek-V4-Flash-Vision-Exp | Llama-3.1-70B |
|---|---|---|
| Developer | DeepSeek | Meta |
| Family | Deepseek V4 Flash Vision Exp | Llama 3 1 70b |
| Model | DeepSeek-V4-Flash-Vision-Exp | Llama-3.1-70B |
| Version | DeepSeek-V4-Flash-Vision-Exp | Llama-3.1-70B |
| Lifecycle | retired | active |
| Released | 2026-08-21 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 131K |
| Total parameters | 304.6B | 70.6B |
| Active parameters | Unknown | Unknown |
| License | mit | llama3.1 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard) | Hugging Face (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | generation |
DeepSeek V4 Flash Vision Exp Capabilities
Llama 3.1 70B Capabilities
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash Vision Exp vs Llama 3.1 70B FAQs
Is DeepSeek V4 Flash Vision Exp or Llama 3.1 70B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash Vision Exp and Llama 3.1 70B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
Only DeepSeek V4 Flash Vision Exp has a directly sourced input price: $0.22 per million tokens. Only DeepSeek V4 Flash Vision Exp has a directly sourced output price: $0.66 per million tokens.
Which has a larger context window, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
DeepSeek V4 Flash Vision Exp has the larger sourced context window. DeepSeek V4 Flash Vision Exp supports 1,049K and Llama 3.1 70B supports 131K.
Which performs better in benchmarks, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Flash Vision Exp or Llama 3.1 70B be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Flash Vision Exp is open weight; Llama 3.1 70B is open weight.
Can DeepSeek V4 Flash Vision Exp and Llama 3.1 70B understand images?+
DeepSeek V4 Flash Vision Exp is documented with image input; Llama 3.1 70B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
Neither has a larger sourced maximum output. DeepSeek V4 Flash Vision Exp is 393K and Llama 3.1 70B is —.
Do DeepSeek V4 Flash Vision Exp and Llama 3.1 70B support reasoning and tool use?+
DeepSeek V4 Flash Vision Exp: reasoning, tool calling, and image input. Llama 3.1 70B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
DeepSeek V4 Flash Vision Exp has 3 sourced provider routes; Llama 3.1 70B has 1, so DeepSeek V4 Flash Vision Exp has broader tracked availability.
Which offers better value, DeepSeek V4 Flash Vision Exp or Llama 3.1 70B?+
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.