Skip to content

DeepSeek V4 vs Llama 4 Maverick

Comprehensive comparison between DeepSeek's DeepSeek V4 and Meta's Llama 4 Maverick. Compare pricing, performance, features, and user reviews.

deepseek vs llamadeepseek v4 vs llama 4open source ai comparison

DeepSeek V4

DeepSeek

DeepSeek V4 (released 2026-04-24) ships two MIT-licensed MoE variants: V4-Pro (1.6T/49B active) and V4-Flash (284B/13B active), both with 1M-token context and hybrid Compressed Sparse Attention + Heavily Compressed Attention. Three reasoning modes (Non-think / Think High / Think Max). V4-Pro uses only 27% of V3.2's FLOPs and 10% of its KV cache at 1M context. Priced well below GPT-5.5 / Opus 4.7 while matching them on most benchmarks.

$1.74/$3.48per M tokens
Details

Llama 4 Maverick

Meta

Meta's flagship open-source multimodal model. 17B active parameters with 400B total (128 expert MoE). 1M context window, natively multimodal with early fusion. Extremely cost-effective at $0.15/$0.60 per M tokens. Supports 12 languages.

$0.15/$0.6per M tokens
Details

Specs Comparison

SpecificationDeepSeek V4Llama 4 Maverick
Context Window1000K1049K
Max Output66K16K
Input (per 1M tokens)$1.74$0.15
Output (per 1M tokens)$3.48$0.60
Reasoning
Open Source

Scenario Score Comparison

DeepSeek V4

Pros

  • + 1M token context window with aggressive KV-cache compression
  • + MIT license — fully open-source, self-hostable
  • + V4-Pro $1.74/$3.48 per MTok — far cheaper than GPT-5.5 and Opus 4.7
  • + New SOTA for open models on SimpleQA-Verified (57.9)
  • + OpenAI + Anthropic API-compatible endpoints
  • + Three reasoning modes tunable per request

Cons

  • − Still trails GPT-5.4 / Gemini 3.1 Pro by 3-6 months on frontier benchmarks
  • − Servers in China (overseas latency, geopolitical concerns)
  • − Text-only — V3's multimodal (image/video) capability not confirmed for V4
  • − V4-Pro self-hosting needs substantial hardware (49B active × FP4/FP8)

Llama 4 Maverick

Pros

  • + Extremely affordable ($0.15/$0.60)
  • + 1M context window
  • + Native multimodal (text + image)
  • + Open source (Llama 4 Community License)
  • + High throughput MoE architecture

Cons

  • − Coding performance below Claude/GPT
  • − Benchmark gaming controversy
  • − 16K max output limit
  • − Knowledge cutoff August 2024

Recommendation

Choose DeepSeek V4 if you:

  • • Need 1m token context window with aggressive kv-cache compression
  • • Need mit license — fully open-source, self-hostable
  • • Need v4-pro $1.74/$3.48 per mtok — far cheaper than gpt-5.5 and opus 4.7

Choose Llama 4 Maverick if you:

  • • Need extremely affordable ($0.15/$0.60)
  • • Need 1m context window
  • • Need native multimodal (text + image)

Based on scores across 0 scenarios, both models perform equally well.

DeepSeek V4 Details Llama 4 Maverick Details

Get Started with DeepSeek V4

1Visit the provider's website
2Create an account
3Start using the model

Get Started with Llama 4 Maverick

1Try via OpenRouter or Together AI
2Or self-host (open source)

💡 Open source - can be self-hosted or used via API providers.

Want to compare other models?

Custom Comparison