DeepSeek V4 Flash Base vs Llama 3.1 8B Instruct
At a Glance
| Compare | DeepSeek V4 Flash BaseDeepSeek | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.050Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $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
Side-by-Side Facts
| Field | DeepSeek-V4-Flash-Base | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | DeepSeek | Meta |
| Family | Deepseek V4 Flash Base | Llama 3 1 8b Instruct |
| Model | DeepSeek-V4-Flash-Base | Llama-3.1-8B-Instruct |
| Version | DeepSeek-V4-Flash-Base | Llama-3.1-8B-Instruct |
| Lifecycle | active | 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 | 292B | 8B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.1 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | generation | chat, generation, tools |
DeepSeek V4 Flash Base Capabilities
Llama 3.1 8B Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash Base vs Llama 3.1 8B Instruct FAQs
Is DeepSeek V4 Flash Base 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 Base 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 Base or Llama 3.1 8B Instruct?+
Only Llama 3.1 8B Instruct has a directly sourced input price: $0.050 per million tokens. Only Llama 3.1 8B Instruct has a directly sourced output price: $0.080 per million tokens.
Which has a larger context window, DeepSeek V4 Flash Base or Llama 3.1 8B Instruct?+
DeepSeek V4 Flash Base has the larger sourced context window. DeepSeek V4 Flash Base supports 1,049K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, DeepSeek V4 Flash Base 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 Base or Llama 3.1 8B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Flash Base is open weight; Llama 3.1 8B Instruct is open weight.
Can DeepSeek V4 Flash Base and Llama 3.1 8B Instruct understand images?+
DeepSeek V4 Flash Base 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 Base or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. DeepSeek V4 Flash Base is — and Llama 3.1 8B Instruct is —.
Do DeepSeek V4 Flash Base and Llama 3.1 8B Instruct support reasoning and tool use?+
DeepSeek V4 Flash Base: none of these features are definitively sourced. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash Base or Llama 3.1 8B Instruct?+
DeepSeek V4 Flash Base has 0 sourced provider routes; Llama 3.1 8B Instruct has 2, so Llama 3.1 8B Instruct has broader tracked availability.
Which offers better value, DeepSeek V4 Flash Base 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.