Llama 5 vs DeepSeek V4 vs Qwen 3.5: Open Models 2026
Llama 5 vs DeepSeek V4 vs Qwen 3.5
Three open-weight model families dominate April 2026: Meta Llama 5 (April 8), DeepSeek V4 (February), and Alibaba Qwen 3.5 (November 2025). They solve different problems. Here’s the real comparison.
Last verified: April 11, 2026
Quick Comparison
| Feature | Llama 5 | DeepSeek V4 | Qwen 3.5 |
|---|---|---|---|
| Released | April 8, 2026 | February 2026 | November 2025 |
| Flagship params | 600B MoE | 685B MoE | 72B dense |
| Active params | ~60B | ~37B | 72B |
| Context window | 5M | 256K | 128K |
| License | Llama Community | MIT | Apache 2.0 |
| Best for | Peak quality | Cost/performance | Local/edge |
Benchmark Showdown
| Benchmark | Llama 5 600B | DeepSeek V4 | Qwen 3.5 72B |
|---|---|---|---|
| MMLU-Pro | 82% | 80% | 74% |
| GPQA Diamond | 78% | 74% | 63% |
| SWE-bench Verified | 74% | 70% | 51% |
| LiveCodeBench | 68% | 66% | 54% |
| Aider Polyglot | 72% | 68% | 58% |
| MATH-500 | 94% | 93% | 88% |
Llama 5 wins across the board, but DeepSeek V4 is surprisingly close — within 2-5 points on most benchmarks. Qwen 3.5 is further back but remember it’s an order of magnitude smaller.
Cost Comparison (Hosted, Together/Fireworks)
| Model | Input / Output per 1M tokens |
|---|---|
| Llama 5 600B | $3.50 / $7.00 |
| DeepSeek V4 | $2.10 / $4.20 |
| Qwen 3.5 72B | $0.90 / $0.90 |
DeepSeek V4 is about 40% cheaper than Llama 5 for roughly 95% of the quality on most tasks. This is why DeepSeek remains the cost/performance champion.
Context Window
- Llama 5: 5 million tokens (largest in the industry as of April 2026)
- DeepSeek V4: 256K tokens
- Qwen 3.5: 128K tokens
If you need to ingest an entire monorepo, long technical papers, or multi-hour meeting transcripts in a single prompt, only Llama 5 does it. For most normal workloads, 128-256K is plenty.
License Implications
- Llama Community License: free for companies with under 700M MAU. Fine for almost everyone, but technically a restriction that purist open-source advocates dislike. Training data is closed.
- MIT (DeepSeek V4): fully permissive, no MAU limits, no field-of-use restrictions. The most open of the three.
- Apache 2.0 (Qwen 3.5): fully permissive with patent grant. Effectively equivalent to MIT for most commercial users.
For startups scared of scaling past 700M MAU, DeepSeek V4 and Qwen 3.5 have cleaner stories.
Hardware Requirements
| Model | Min serving hardware | Approx. cost |
|---|---|---|
| Llama 5 600B | 8x H100 80GB | $180K new |
| DeepSeek V4 | 8x H100 80GB | $180K new |
| Qwen 3.5 72B | 1x A100 80GB | $15K |
Llama 5 and DeepSeek V4 have nearly identical serving requirements — both need an 8x H100 rig for the flagship variants. Qwen 3.5 72B runs on a single GPU, which is a massive operational advantage.
When to Pick Each
Pick Llama 5 if…
- You need the best possible open-weight quality
- You need 5M token context (long documents, full codebases)
- You already have the GPU budget
- You’re benchmarking against closed frontier models
Pick DeepSeek V4 if…
- You want 95% of Llama 5’s quality at 60% the cost
- You need the cleanest license (MIT)
- You’re cost-sensitive but still want frontier quality
- 256K context is enough for your workloads
Pick Qwen 3.5 if…
- You need to run locally on consumer hardware
- You want the cheapest hosted inference ($0.90/M)
- You’re building edge or on-device AI
- Apache 2.0 is a hard requirement
The Right Answer: Use All Three
Smart teams in April 2026 route requests across all three:
- Llama 5 for the hardest reasoning and long-context work
- DeepSeek V4 for bulk coding and general-purpose chat
- Qwen 3.5 for high-volume classification, extraction, and routing
This hybrid approach can cut total inference costs by 5-10x versus running everything through a single flagship model — closed or open.
Last verified: April 11, 2026