DeepSeek V4 Flash vs Llama 3.1 8B Instruct
At a Glance
| Compare |  DeepSeek V4 FlashDeepSeek | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.0868Openrouter ↗ · Aug 28, 2026 | $0.050Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.1736Openrouter ↗ · Aug 28, 2026 | $0.080Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 131K |
| Model facts checked | Aug 28, 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
| Benchmark | DeepSeek-V4-Flash | Llama-3.1-8B-Instruct |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,431.79100% of row best · rating · deepseek-v4-flash; 95% CI [1427.66110387, 1435.91557810]; votes 48887; rank 83 | 1,186.4883% of row best · rating · llama-3.1-8b-instruct; 95% CI [1182.38141125, 1190.58254982]; votes 49605; rank 313 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 78.00100% of row best · points · DeepSeek V4 Flash (xhigh) · 12,709 output tokens / case | 47.9261% of row best · points · Llama 3.1 8B · 1,165 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | DeepSeek-V4-Flash | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | DeepSeek | Meta |
| Family | Deepseek V4 Flash | Llama 3 1 8b Instruct |
| Model | DeepSeek-V4-Flash | Llama-3.1-8B-Instruct |
| Version | DeepSeek-V4-Flash | Llama-3.1-8B-Instruct |
| Lifecycle | retired | active |
| Released | 2026-04-24 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 131K |
| Total parameters | 290.9B | 8B |
| Active parameters | 13B | 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), Openrouter (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, tools |
DeepSeek V4 Flash Capabilities
Llama 3.1 8B Instruct Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash vs Llama 3.1 8B Instruct FAQs
Is DeepSeek V4 Flash or Llama 3.1 8B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash and Llama 3.1 8B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash or Llama 3.1 8B Instruct?+
DeepSeek V4 Flash is $0.0868 and Llama 3.1 8B Instruct is $0.050 per million tokens, so Llama 3.1 8B Instruct is cheaper on this metric. DeepSeek V4 Flash is $0.1736 and Llama 3.1 8B Instruct is $0.080 per million tokens, so Llama 3.1 8B Instruct is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Flash or Llama 3.1 8B Instruct?+
DeepSeek V4 Flash has the larger sourced context window. DeepSeek V4 Flash supports 1,049K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, DeepSeek V4 Flash or Llama 3.1 8B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Flash or Llama 3.1 8B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Flash is open weight; Llama 3.1 8B Instruct is open weight.
Can DeepSeek V4 Flash and Llama 3.1 8B Instruct understand images?+
DeepSeek V4 Flash is not documented with image input; Llama 3.1 8B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Flash or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. DeepSeek V4 Flash is — and Llama 3.1 8B Instruct is —.
Do DeepSeek V4 Flash and Llama 3.1 8B Instruct support reasoning and tool use?+
DeepSeek V4 Flash: reasoning. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash or Llama 3.1 8B Instruct?+
DeepSeek V4 Flash has 5 sourced provider routes; Llama 3.1 8B Instruct has 2, so DeepSeek V4 Flash has broader tracked availability.
Which offers better value, DeepSeek V4 Flash or Llama 3.1 8B 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.