Mistral · model passport
Mistral 7b Instruct V0 2
mistral-7b-instruct-v0-2- Open weights
- Apache-2.0 licence
- 7.2B params
- Released 2023-12-11(curated)
- Overall · LMArena
- 1,150#358 of 396
- Best percentile
- 10.4Hard prompts
- Cohort standing
- 24.6median 29.2
What the data says
Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.
Weakest axes
- 6.5 pct — LiveBench (19.5%)
- 9.6 pct — Instruction following (1,123)
- 9.8 pct — Overall (1,150)
Uneven: 3.9 percentile points between its best and worst axis — 10.4 pct on Hard prompts versus 6.5 pct on LiveBench.
- Hard prompts10.4 pct1,157Verify →
- Coding10.2 pct1,186Verify →
- Overall9.8 pct1,150Verify →
- Instruction following9.6 pct1,123Verify →
- LiveBench6.5 pct19.5%Verify →
- WebDev ArenaNot benchmarked
- Vision ArenaNot benchmarked
- Factuality ArenaNot benchmarked
- Search ArenaNot benchmarked
- Document ArenaNot benchmarked
- Agent ArenaNot benchmarked
- GPQA DiamondNot benchmarked
- AIME (math)Not benchmarked
- FrontierMathNot benchmarked
- SimpleQA VerifiedNot benchmarked
- ARC-AGI-2Not benchmarked
- Humanity's Last ExamNot benchmarked
- SimpleBenchNot benchmarked
- BFCL v4 (function calling)Not benchmarked
- SWE-bench Verified (live board)Not benchmarked
- Terminal-Bench 4.0Not benchmarked
- MMLU-Pro (Artificial Analysis)Not benchmarked
- LiveCodeBench (Artificial Analysis)Not benchmarked
- IFBench (Artificial Analysis)Not benchmarked
- HHEM Hallucination (Vectara)Not benchmarked
- SWE-bench VerifiedNot benchmarked
- Terminal-BenchNot benchmarked
Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.
How to run it
Run it yourself
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)Ollama
We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →
Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →
Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.
mistral-7b-instruct-v0.2 stands at 24.6 (raw mean 9.3) — measured on 5 of 27 axes.
mistral-7b-instruct-v0.2 stands at 24.6 versus a cohort median of 29.2 across 82 models released within 180 days — below the peer median.
mistral-7b-instruct-v0.2 scores at percentile 6.5 on LiveBench.
mistral-7b-instruct-v0.2 scores at percentile 9.6 on Instruction following.
mistral-7b-instruct-v0.2 ranks between #368 and #965 across 5 built-in weightings — best under "Coding-focused", worst under "All sources".
Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →