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 | 1.8M | 9.7M |
| Last observed | 2026-09-18 | 2026-09-18 |
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
Pick this version over its predecessor for the window: 32,768 tokens of context against 8k in v0.1, with the RoPE base raised to 1e6, on a 7.2 billion parameter instruct model. At 16-bit the bfloat16 weights are 14.5 GB and the memory need is 17.4 GB. At $1.85 per hour on demand, the cheapest card we list, a single 192 GB MI300X, leaves most of its memory free for long prompts and batch. Quantized to 8-bit it needs 8.7 GB.
Apache 2.0 covers it, so commercial use, modification and redistribution are all on the table as long as the notices stay and significant changes are stated. Two things to check. Prompts must be wrapped in [INST] and [/INST] tokens, so your serving layer has to apply that template. And the one benchmark on file, 30.84 on MMLU-Pro, is third-party reported by EvalEval, not the publisher's number.
SAVRN's Notes on Qwen2.5-7B-Instruct
Structured work is what Qwen built this 7.6B instruct model for: reading tables, writing JSON and producing text past 8K tokens, in their words. At 16-bit the run needs 18.3 GB, and the cheapest listed setup, one MI300X with 192 GB at $1.85 an hour on-demand, fits ten copies by simple arithmetic. At 4-bit the need falls to 4.6 GB, small enough to share a card with other services.
Apache 2.0 lets you ship it in a product, fine-tuned or not, with notices kept and significant changes stated. It derives from Qwen/Qwen2.5-7B, so the base is there if you would rather tune it yourself. The page lists 32,768 tokens of context and cites arXiv:2309.00071, the YaRN context-extension paper, so confirm which window your serving stack honors before promising long documents. No host quotes it by the token on the SAVRN Index today; 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.