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Compare / Qwen3-Next 80B-A3B Thinking vs DeepSeek-R1-Distill-Llama-70B

Qwen3-Next 80B-A3B Thinking vs DeepSeek-R1-Distill-Llama-70B

Updated November 2026

Verdict

Qwen3-Next 80B-A3B Thinking scores higher on the Small LM Index: 63.3 vs 50.1. On the 3 benchmarks both labs report, Qwen3-Next 80B-A3B Thinking wins 3 and DeepSeek-R1-Distill-Llama-70B wins 0. DeepSeek-R1-Distill-Llama-70B is smaller: it needs about 47.4 GB of memory at 4-bit, against 50.8 GB for Qwen3-Next 80B-A3B Thinking. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick Qwen3-Next 80B-A3B Thinking if…

  • you want the strongest model overall
  • you mostly write code
  • you need math and reasoning
  • you work with long documents

Pick DeepSeek-R1-Distill-Llama-70B if…

  • you have limited memory (about 47.4 GB at 4-bit)

Specifications

Qwen3-Next 80B-A3B ThinkingDeepSeek-R1-Distill-Llama-70B
Small LM Index63.3 (likely 59–67)50.1 (likely 45–55)
LabAlibaba (Qwen) · ChinaDeepSeek · China
Released2025-09-112025-01-20
Parameters80B (3B active, MoE)70.6B (dense)
Memory at 4-bit~50.8 GB~47.4 GB
Context window262K tokens128K tokens
Inputtexttext
ReasoningAlways thinksAlways thinks
LicenseApache-2.0MIT
API price / 1M tokensSelf-host onlySelf-host only
Hugging Face downloads (30 days)43.8K30.5K

Benchmarks

Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.

BenchmarkQwen3-Next 80B-A3B ThinkingDeepSeek-R1-Distill-Llama-70B
MMLU-Pro Knowledge82.7≈ 72
GPQA Diamond Knowledge77.265.2
AIME Math87.870.0
LiveCodeBench Coding68.757.5
IFBench Instruction following61.5—

Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.

Questions

Which is better, Qwen3-Next 80B-A3B Thinking or DeepSeek-R1-Distill-Llama-70B?

Qwen3-Next 80B-A3B Thinking scores higher on the Small LM Index: 63.3 vs 50.1. On the 3 benchmarks both labs report, Qwen3-Next 80B-A3B Thinking wins 3 and DeepSeek-R1-Distill-Llama-70B wins 0.

Which is cheaper to run?

Neither model has a hosted API on OpenRouter, so you would run both yourself. DeepSeek-R1-Distill-Llama-70B is smaller: it needs about 47.4 GB of memory at 4-bit, against 50.8 GB for Qwen3-Next 80B-A3B Thinking.

Which has the longer context window?

Qwen3-Next 80B-A3B Thinking has the longer context window: 262K tokens against 128K.

Can I use Qwen3-Next 80B-A3B Thinking and DeepSeek-R1-Distill-Llama-70B commercially?

Qwen3-Next 80B-A3B Thinking is released under Apache-2.0 and DeepSeek-R1-Distill-Llama-70B under MIT. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.

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