Codestral 22B v0.1 vs Qwen 2.5 Coder 7B

The two models at a glance

About Codestral 22B v0.1

Mistral AI's 22B code specialist covering 80+ programming languages, with strong HumanEval and MBPP scores. Locked behind the restrictive MNPL license — personal and research use only. Strengths: HumanEval 81.1 and MBPP 78.2 — competitive with much larger models at release, Broad language coverage including niche languages, 32k context handles most repo files comfortably, Strong fill-in-the-middle completion.

About Qwen 2.5 Coder 7B

A 7B coding specialist from Alibaba covering 92 programming languages with a 128k context. Competitive with proprietary models on HumanEval at this size. Strengths: Strong HumanEval and code completion for a 7B, 128k context for repo-scale prompts, Coverage of 92 programming languages, Apache 2.0 license.

How they compare

Codestral 22B v0.1 comes from Mistral AI and Qwen 2.5 Coder 7B from Alibaba, they belong to the Mistral and Qwen families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 22B vs 7B parameters, Codestral 22B v0.1 is the larger of the two. At Q4, Qwen 2.5 Coder 7B fits in about 5 GB of VRAM versus 13 GB for the other — a 8 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Qwen 2.5 Coder 7B takes HumanEval with 88.4 against 81.1 — a clear 7.3-point margin. On MBPP the edge goes to Qwen 2.5 Coder 7B (83.5 vs 78.2). For workloads weighted toward that benchmark, Qwen 2.5 Coder 7B is the stronger default.

On a typical mid-range GPU, Qwen 2.5 Coder 7B pushes roughly 35 tokens/sec versus 16, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 2.5 Coder 7B offers the bigger window (128k vs 31k tokens).

Memory, quantization & throughput

Across quantization levels, Codestral 22B v0.1 requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 24 GB, FP16 ≈ 44 GB, while Qwen 2.5 Coder 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice Codestral 22B v0.1 needs a 16 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Codestral 22B v0.1 needs roughly 22 GB of system RAM to run on CPU and Qwen 2.5 Coder 7B about 8 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 16 tokens/sec from Codestral 22B v0.1 and 35 from Qwen 2.5 Coder 7B, scaling up to 42 and 90 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Codestral 22B v0.1 or Qwen 2.5 Coder 7B to the card you actually own:

• On a 8 GB GPU: Codestral 22B v0.1 does not fit; Qwen 2.5 Coder 7B runs at Q5 (6 GB).
• On a 12 GB GPU: Codestral 22B v0.1 does not fit; Qwen 2.5 Coder 7B runs at Q8 (9 GB).
• On a 16 GB GPU: Codestral 22B v0.1 runs at Q5 (16 GB); Qwen 2.5 Coder 7B runs at FP16 (16 GB).
• On a 24 GB GPU: Codestral 22B v0.1 runs at Q8 (24 GB); Qwen 2.5 Coder 7B runs at FP16 (16 GB).

Benchmark scores

Reported benchmarks for Codestral 22B v0.1: HumanEval 81.1, MBPP 78.2, Spider 63.5.

Reported benchmarks for Qwen 2.5 Coder 7B: HumanEval 88.4, MBPP 83.5.

Bottom line: which should you pick?

• Pick Qwen 2.5 Coder 7B if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Qwen 2.5 Coder 7B for long-context work (up to 128k tokens).
• Pick Qwen 2.5 Coder 7B for lower VRAM and faster inference; pick Codestral 22B v0.1 for maximum headline quality.
• Pick Qwen 2.5 Coder 7B if HumanEval performance is your priority (88.4 vs 81.1).
• Pick Codestral 22B v0.1 if your workload is fr.

Which GPU should you buy to run Codestral 22B v0.1?

To run Codestral 22B v0.1 locally at Q4, you need ~13 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).

Frequently asked questions

What is the difference between Codestral 22B v0.1 and Qwen 2.5 Coder 7B?

The headline differences: Codestral 22B v0.1 is a 22B model and Qwen 2.5 Coder 7B is 7B; their context windows differ (31k vs 128k tokens); they ship under different licenses (Mistral Non-Production License vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Codestral 22B v0.1 and Qwen 2.5 Coder 7B run on a 24 GB GPU?

At a Q4 quantization, Codestral 22B v0.1 needs about 13 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 2.5 Coder 7B needs about 5 GB and fits comfortably on a 24 GB GPU. Qwen 2.5 Coder 7B is the lighter option for tight VRAM budgets.

Codestral 22B v0.1 vs Qwen 2.5 Coder 7B for coding — which is better?

On HumanEval, Qwen 2.5 Coder 7B leads with 88.4 vs 81.1 (a 7.3-point gap), making it the stronger pick for code generation.

Which is faster, Codestral 22B v0.1 or Qwen 2.5 Coder 7B?

Qwen 2.5 Coder 7B is the smaller model (7B vs 22B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Codestral 22B v0.1 or Qwen 2.5 Coder 7B?

Qwen 2.5 Coder 7B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Mistral Non-Production License — check its terms before commercial deployment.

Which has the longer context window, Codestral 22B v0.1 or Qwen 2.5 Coder 7B?

Qwen 2.5 Coder 7B has the larger context window (128k vs 31k tokens), so it handles longer documents and codebases in a single prompt.