DeepSeek V4 Pro 0813 vs Llama 4 Maverick 17B 128E Instruct
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | DeepSeek-V4-Pro-0813 | Llama-4-Maverick-17B-128E-Instruct |
|---|---|---|
| Developer | DeepSeek | Meta |
| Family | Deepseek V4 Pro | Llama 4 Maverick 17b 128e Instruct |
| Model | DeepSeek-V4-Pro-0813 | Llama-4-Maverick-17B-128E-Instruct |
| Version | DeepSeek-V4-Pro-0813 | Llama-4-Maverick-17B-128E-Instruct |
| Lifecycle | active | active |
| Released | 2026-08-13 | 2025-04-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | 1.6T | 401.6B |
| Active parameters | 49B | 17B |
| License | mit | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard) | Openrouter (Standard) |
| Capabilities | agents, chat, coding, generation, reasoning, tools | chat, generation, tools |
17 comparable fields · 12 material differences · Pair passes the primary-source comparison gate
DeepSeek V4 Pro 0813 Capabilities
Llama 4 Maverick 17B 128E Instruct Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Pro 0813 vs Llama 4 Maverick 17B 128E Instruct FAQs
Is DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro 0813 and Llama 4 Maverick 17B 128E Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct?+
DeepSeek V4 Pro 0813 is $1.30 and Llama 4 Maverick 17B 128E Instruct is $0.20 per million tokens, so Llama 4 Maverick 17B 128E Instruct is cheaper on this metric. DeepSeek V4 Pro 0813 is $2.60 and Llama 4 Maverick 17B 128E Instruct is $0.696 per million tokens, so Llama 4 Maverick 17B 128E Instruct is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct?+
DeepSeek V4 Pro 0813 has the larger sourced context window. DeepSeek V4 Pro 0813 supports 1,049K and Llama 4 Maverick 17B 128E Instruct supports 1,000K.
Which performs better in benchmarks, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Pro 0813 is open weight; Llama 4 Maverick 17B 128E Instruct is open weight.
Can DeepSeek V4 Pro 0813 and Llama 4 Maverick 17B 128E Instruct understand images?+
DeepSeek V4 Pro 0813 is not documented with image input; Llama 4 Maverick 17B 128E Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct?+
Neither has a larger sourced maximum output. DeepSeek V4 Pro 0813 is — and Llama 4 Maverick 17B 128E Instruct is —.
Do DeepSeek V4 Pro 0813 and Llama 4 Maverick 17B 128E Instruct support reasoning and tool use?+
DeepSeek V4 Pro 0813: reasoning and tool calling. Llama 4 Maverick 17B 128E Instruct: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E Instruct?+
DeepSeek V4 Pro 0813 has 3 sourced provider routes; Llama 4 Maverick 17B 128E Instruct has 1, so DeepSeek V4 Pro 0813 has broader tracked availability.
Which offers better value, DeepSeek V4 Pro 0813 or Llama 4 Maverick 17B 128E 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.