Model crosswalk
Side-by-side on price, capability and workload — three-way comparison.
DeepSeek R1 Distill Llama 70B
vs
Qwen 2.5 Coder 32B Instruct
vs
Refact Llama 3.1 70B
DeepSeek R1 Distill Llama 70BA
DeepSeek R1 Distill Llama 70B
70B params · 131K context · mit
Cheapest providerdeepinfra
$/1M input$280000.00
$/1M output$550000.00
Qwen 2.5 Coder 32B InstructB
Qwen 2.5 Coder 32B Instruct
32B params · 131K context · qwen
Cheapest providerdeepinfra
$/1M input$120000.00
$/1M output$250000.00
Refact Llama 3.1 70BC
Refact Llama 3.1 70B
70B params · 131K context · llama-3
Cheapest provider—
$/1M input—
$/1M output—
Specs and cheapest providers
| Spec | DeepSeek R1 Distill Llama 70B | Qwen 2.5 Coder 32B Instruct | Refact Llama 3.1 70B |
|---|---|---|---|
| Parameters | 70B | 32B | 70B |
| Context window | 131K tokens | 131K tokens | 131K tokens |
| License | mit | qwen | llama-3 |
| Released | 2025-01-20 | 2024-11-12 | 2024-09-01 |
| Cheapest provider | |||
| Provider | deepinfra | deepinfra | — |
| Input / 1M tokens | $280000.00 | $120000.00🏆 | — |
| Output / 1M tokens | $550000.00 | $250000.00🏆 | — |
Benchmark comparison
No benchmark data available yet.
Editor's take
A reasoning-distilled generalist, a code-benchmark leader, and a fine-tune targeting IDE pipelines — three 32–70B models optimized for meaningfully different tasks.
DeepSeek R1 Distill Llama 70B, released January 2025, was produced by distilling reasoning-chain supervision from DeepSeek's full 671B R1 MoE into a Llama 3.3 70B base. Independent benchmarks place it at roughly 70–80 percent of full R1's score on AIME and MATH. For code tasks, it applies chain-of-thought reasoning rather than raw code-specialist fine-tuning, which means it handles algorithmic problem-solving well but may lag purpose-built coders on autocomplete-style completions. Groq's hardware makes it one of the faster 70B options for latency-sensitive requests. MIT license — fully commercial, no usage restrictions.
Qwen 2.5 Coder 32B Instruct, released November 2024 by Alibaba, is explicitly optimized for code: 32 billion parameters, 92 programming languages, and a 131K context window that handles multi-file codebases and larger diffs in a single pass. On LiveCodeBench and MultiPL-E it benchmarks alongside DeepSeek Coder V2. It does not do chain-of-thought reasoning in the same vein as R1 Distill, but for completion-style, agentic pipelines, and CI code generation it is the sharper tool. Qwen license covers commercial use.
Refact Llama 3.1 70B, co-released by Together Computer and Refact AI in September 2024, is a fine-tune of Llama 3.1 70B focused on IDE tab-completion and agentic refactoring rather than general code generation. Its 128K context window is the key specification — it ingests large file trees for multi-file diffs without chunking. This model makes sense only if your product is an IDE extension or a refactoring agent; for general code generation pipelines, Qwen 2.5 Coder 32B or R1 Distill offer broader utility. Inherits the Llama 3 community license.
Pick DeepSeek R1 Distill 70B for reasoning-heavy code problems that benefit from chain-of-thought. Pick Qwen 2.5 Coder 32B for high-throughput production code generation and CI pipelines. Pick Refact Llama 3.1 70B specifically when building an IDE-as-a-product or agent loop that needs deep file-tree context.
Compare two at a time
Frequently asked questions
- How does DeepSeek R1 Distill Llama 70B compare to Qwen 2.5 Coder 32B Instruct and Refact Llama 3.1 70B on price?
- Use the table above to compare input and output prices per 1M tokens across the cheapest available providers for each model.
- Which model is best for coding: DeepSeek R1 Distill Llama 70B, Qwen 2.5 Coder 32B Instruct, or Refact Llama 3.1 70B?
- HumanEval and other code benchmarks are shown in the table. For production code tasks, also consider context window size and provider latency.
- What is the context window for DeepSeek R1 Distill Llama 70B, Qwen 2.5 Coder 32B Instruct, and Refact Llama 3.1 70B?
- Context window sizes are listed in the Specs row of the comparison table above.
Full model details