SAVRN Model Hub · Comparisons
Mistral-7B-Instruct-v0.2 vs Qwen2.5-7B-Instruct
Mistral-7B-Instruct-v0.2 has 7.2B parameters and Qwen2.5-7B-Instruct has 7.6B parameters; both are released under Apache License 2.0; at 16-bit, Mistral-7B-Instruct-v0.2 needs about 17.4 GB (1x MI300X from $1.85 an hour) and Qwen2.5-7B-Instruct about 18.3 GB (1x MI300X from $1.85 an hour).
| Field | Mistral-7B-Instruct-v0.2 mistralai/Mistral-7B-Instruct-v0.2 | Qwen2.5-7B-Instruct Qwen/Qwen2.5-7B-Instruct |
|---|---|---|
| Publisher | Mistral AI_ | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 7.2B parameters | 7.6B parameters |
| Architecture | MistralForCausalLM | Qwen2ForCausalLM |
| Library | transformers | transformers |
| Context length | 32,768 tokens | 32,768 tokens |
| Repository size | 29.5 GB | 15.2 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | apache-2.0 | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 17.4 GB | 18.3 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 4.3 GB | 4.6 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 63a8b0818953 | a09a35458c70 |
| Downloads reported by the hub | 1.6M | 8.1M |
| Last observed | 2026-10-10 | 2026-10-10 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
Mistral-7B-Instruct-v0.2
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 30.84 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
Qwen2.5-7B-Instruct
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| LEXam-Benchmark/LEXam | Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established | 29.28 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| LEXam-Benchmark/LEXam | Task open_questionMetric open_questionComparison conditions not established | 16.67 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| thamilvendhan/signalbench | Task access_denyMetric access_denySetup family=access_deny; n=12Comparison conditions not established | 0.1667 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task bot_policyMetric bot_policySetup family=bot_policy; n=12Comparison conditions not established | 0.3333 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task injectionMetric injectionSetup family=injection; n=12Comparison conditions not established | 0.5 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task memory_labelMetric memory_labelSetup family=memory_label; n=12Comparison conditions not established | 0.5 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task srcMetric srcSetup SRC overall; deterministic action-based grader, no LLM judge; seed 0, n=75Comparison conditions not established | 0.3833 | signalbench raw per-item responses Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
| thamilvendhan/signalbench | Task timeMetric timeSetup family=time; n=12Comparison conditions not established | 0.4167 | thamilvendhan Reported by a third party |
Evaluated revision not stated | 2026-07-08 |
SAVRN's Notes on Mistral-7B-Instruct-v0.2
The predecessor to v0.3 on this list, with the same 7.2B-parameter body, the same 32,768-token context and the same memory lines — 17.4 GB at 16-bit, 8.7 GB at 8-bit, 4.3 GB at 4-bit on a cheapest setup of one MI300X at $1.85 an hour. What differs is momentum: v0.2 draws 1.7M downloads a month against v0.3's 2.2M, and its weights last moved 2025-07-24.
Apache 2.0 permits commercial use with notices kept. A third-party EvalEval result on the page puts MMLU-Pro at 30.84 — a number that says 2023 instruct model, not current reasoning model. The 29.5 GB repo duplicates weights in bin and safetensors. Unless a pinned evaluation or a downstream adapter holds you to v0.2, standardize on v0.3 and keep this for regression testing.
SAVRN's Notes on Qwen2.5-7B-Instruct
7.6B parameters, 18.3 GB at 16-bit, 4.6 GB at 4-bit, on one 192 GB MI300X at $1.85 an hour — a single card serves this at any precision, which is why it stays the default self-hosted mid-size workhorse at 9.7M downloads a month. Qwen built the 2.5 instruct line for structured work: reading tables, writing JSON, generating past 8K tokens.
Apache 2.0 lets you ship it in a product, fine-tuned or not. The official quantized build Qwen2.5-7B-Instruct-AWQ is listed on the page and is the sane 4-bit pick over community conversions. The page states 32,768 tokens of context and cites the YaRN context-extension paper (arXiv:2309.00071), so confirm which window your serving stack actually honors. No host quotes it by the token on our Index — the card hour is your cost basis.
Questions
Which is larger, Mistral-7B-Instruct-v0.2 or Qwen2.5-7B-Instruct?
Qwen2.5-7B-Instruct (7.6B parameters) is larger than Mistral-7B-Instruct-v0.2 (7.2B parameters), by the parameter counts their publishers report.
Which is cheaper to run, Mistral-7B-Instruct-v0.2 or Qwen2.5-7B-Instruct?
At 4-bit, Mistral-7B-Instruct-v0.2 fits on 1x MI300X from $1.85 an hour and Qwen2.5-7B-Instruct on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Mistral-7B-Instruct-v0.2 commercially?
Yes. Mistral-7B-Instruct-v0.2 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Can I use Qwen2.5-7B-Instruct commercially?
Yes. Qwen2.5-7B-Instruct is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.