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Compare / Qwen3-Coder-Next vs DeepSeek-R1-Distill-Llama-70B

Qwen3-Coder-Next vs DeepSeek-R1-Distill-Llama-70B

Updated November 2026

Verdict

Qwen3-Coder-Next scores higher on the Small LM Index: 63.1 vs 50.0. On the 5 benchmarks both labs report, Qwen3-Coder-Next wins 5 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 50.6 GB for Qwen3-Coder-Next. Only Qwen3-Coder-Next has a hosted API on OpenRouter ($0.29 per million tokens); the other has to be self-hosted.

Pick Qwen3-Coder-Next if…

  • you want the strongest model overall
  • you need precise instruction following
  • you build agents that call tools
  • you want a hosted API rather than running it yourself
  • you work with long documents
  • you want the more widely used model (more quantized builds and community support)

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

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

Specifications

Qwen3-Coder-NextDeepSeek-R1-Distill-Llama-70B
Small LM Index63.1 (likely 54–70)50.0 (likely 47–53)
LabAlibaba (Qwen) · ChinaDeepSeek · China
Released2026-01-302025-01-20
Parameters79.7B (3B active, MoE)70.6B (dense)
Memory at 4-bit~50.6 GB~47.4 GB
Context window262K tokens128K tokens
Inputtexttext
ReasoningNo thinkingAlways thinks
LicenseApache-2.0MIT
API price / 1M tokens$0.29 ($0.12 in, $0.80 out)Self-host only
Hugging Face downloads (30 days)430.1K30K

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-Coder-NextDeepSeek-R1-Distill-Llama-70B
MMLU-Pro Knowledge≈ 8179.5✓
GPQA Diamond Knowledge73.7✓40.2✓
Humanity's Last Exam Reasoning10.1✓5.1✓
AIME Math≈ 8767.0✓
LiveCodeBench Coding≈ 7326.6✓
SWE-bench Verified Coding70.6≈ 47
Terminal-Bench Agentic36.2—
τ²-Bench Agentic79.5✓21.9✓
IFBench Instruction following35.2✓27.6✓
MATH-500 Math—93.5✓
AA-LCR Long context47.0✓10.0✓
SciCode Coding36.2✓—

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-Coder-Next or DeepSeek-R1-Distill-Llama-70B?

Qwen3-Coder-Next scores higher on the Small LM Index: 63.1 vs 50.0. On the 5 benchmarks both labs report, Qwen3-Coder-Next wins 5 and DeepSeek-R1-Distill-Llama-70B wins 0.

Which is cheaper to run?

Only Qwen3-Coder-Next has a hosted API on OpenRouter ($0.29 per million tokens); the other has to be self-hosted. DeepSeek-R1-Distill-Llama-70B is smaller: it needs about 47.4 GB of memory at 4-bit, against 50.6 GB for Qwen3-Coder-Next.

Which has the longer context window?

Qwen3-Coder-Next has the longer context window: 262K tokens against 128K.

Can I use Qwen3-Coder-Next and DeepSeek-R1-Distill-Llama-70B commercially?

Qwen3-Coder-Next 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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