Mistral AIopen-weight
Released: 2026-06-01Mistral: Mistral Medium 3.5
Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...
Intelligence Index
88.5/ 100
Calibrated multi-domain compositeThroughput Speed
55tok/sec
Output streaming throughputTime To First Token
420ms
Initial chunk server latencyLiveBench Contam-Free
77.9%
September 2026 suite scoreLiveBench Multi-Domain Evaluation Breakdown
Monthly refreshed contamination-free test setsLogical Reasoning81.4%
Coding & Repo Repair79.7%
Mathematics (AIME)78.8%
Data Analysis & Tables75.2%
Language & Comprehension77.0%
Instruction Following77.9%
Sourced Benchmark Evaluations (61)
Verified performance across authoritative benchmarks with provenance tracking.| Benchmark | Status | Score | Trust | Date | Source Type | Provenance |
|---|---|---|---|---|---|---|
| ARC-AGI-3 | Active | 55.8% | 93 | 2026-07-06 | independent | Source ↗ |
| ARC-AGI-2 | Nearing Saturation | 66.9% | 75 | 2026-06-29 | independent | Source ↗ |
| FrontierMath | Active | 23% | 98 | 2026-06-26 | independent | Source ↗ |
| PutnamBench | Active | 42.8% | 90 | 2026-06-25 | independent | Source ↗ |
| SWE-Lancer | Deprecated | 57.2% | 20 | 2026-06-23 | independent | Source ↗ |
| Omni-MATH | Deprecated | 60.9% | 10 | 2026-06-23 | independent | Source ↗ |
| Soohak | Active | 62.5% | 98 | 2026-06-21 | independent | Source ↗ |
| FrontierCode | Active | 48.8% | 98 | 2026-06-21 | independent | Source ↗ |
| Humanity's Last Exam | Active | 37.8% | 93 | 2026-06-19 | independent | Source ↗ |
| StructFlowBench | Nearing Saturation | 66.2% | 75 | 2026-06-19 | independent | Source ↗ |
| AIME | Nearing Saturation | 78.2% | 75 | 2026-06-19 | independent | Source ↗ |
| OlympiadBench | Active | 67.5% | 80 | 2026-06-18 | independent | Source ↗ |
| MathArena | Active | 65.8% | 99 | 2026-06-17 | independent | Source ↗ |
| FireBench | Active | 65.4% | 95 | 2026-06-17 | independent | Source ↗ |
| Terminal-Bench | Active | 58.9% | 80 | 2026-06-17 | independent | Source ↗ |
| OpenAI-MRCR | Active | 74.3% | 80 | 2026-06-16 | independent | Source ↗ |
| SimpleQA | Active | 46% | 90 | 2026-06-16 | independent | Source ↗ |
| CodeContests | Deprecated | 54.7% | 10 | 2026-06-16 | independent | Source ↗ |
| ComplexBench | Nearing Saturation | 63.9% | 80 | 2026-06-15 | independent | Source ↗ |
| SWE-bench Pro | Active | 52.1% | 90 | 2026-06-15 | independent | Source ↗ |
| LongBench-Pro | Active | 72.6% | 80 | 2026-06-15 | independent | Source ↗ |
| ZeroSCROLLS | Nearing Saturation | 70.8% | 78 | 2026-06-14 | independent | Source ↗ |
| MultiPL-E | Deprecated | 77.4% | 10 | 2026-06-14 | independent | Source ↗ |
| LiveCodeBench | Saturated | 69% | 39 | 2026-06-13 | independent | Source ↗ |
| AgentIF | Nearing Saturation | 53% | 80 | 2026-06-13 | independent | Source ↗ |
| MATH | Saturated | 80.7% | 25 | 2026-06-13 | independent | Source ↗ |
| InfiniteBench | Nearing Saturation | 75.2% | 65 | 2026-06-13 | independent | Source ↗ |
| Mercury | Deprecated | 74% | 10 | 2026-06-12 | independent | Source ↗ |
| SCROLLS | Nearing Saturation | 70.8% | 65 | 2026-06-12 | independent | Source ↗ |
| MMLU-Pro | Nearing Saturation | 81.4% | 75 | 2026-06-12 | independent | Source ↗ |
| RULER | Nearing Saturation | 81.4% | 84 | 2026-06-11 | independent | Source ↗ |
| TabMWP | Saturated | 75.7% | 25 | 2026-06-11 | independent | Source ↗ |
| CyberSecEval | Deprecated | 75.7% | 10 | 2026-06-11 | independent | Source ↗ |
| SWE-bench Verified | Saturated | 65.6% | 25 | 2026-06-10 | independent | Source ↗ |
| MMLU-Redux | Saturated | 86.7% | 25 | 2026-06-10 | independent | Source ↗ |
| QuALITY | Nearing Saturation | 77.9% | 78 | 2026-06-10 | independent | Source ↗ |
| LongBench | Nearing Saturation | 76.1% | 65 | 2026-06-09 | independent | Source ↗ |
| SWE-bench | Deprecated | 57.2% | 10 | 2026-06-09 | independent | Source ↗ |
| MGSM | Nearing Saturation | 78.2% | 65 | 2026-06-09 | independent | Source ↗ |
| AGIEval | Saturated | 73.8% | 25 | 2026-06-08 | independent | Source ↗ |
| QASPER | Nearing Saturation | 73.5% | 65 | 2026-06-08 | independent | Source ↗ |
| TruthfulQA | Saturated | 77.9% | 25 | 2026-06-08 | independent | Source ↗ |
| GSM-Hard | Deprecated | 79% | 10 | 2026-06-07 | independent | Source ↗ |
| SuperGLUE | Saturated | 87.5% | 25 | 2026-06-07 | independent | Source ↗ |
| CodeXGLUE | Deprecated | 79.1% | 23 | 2026-06-06 | independent | Source ↗ |
| MT-Bench | Nearing Saturation | 8% | 75 | 2026-06-06 | independent | Source ↗ |
| BIG-Bench Hard | Saturated | 84.1% | 25 | 2026-06-06 | independent | Source ↗ |
| WinoGrande | Saturated | 89.2% | 28 | 2026-06-05 | independent | Source ↗ |
| MBPP+ | Nearing Saturation | 85.8% | 65 | 2026-06-05 | independent | Source ↗ |
| IFEval | Deprecated | 76.3% | 25 | 2026-06-05 | independent | Source ↗ |
| GPQA | Nearing Saturation | 77.9% | 78 | 2026-06-05 | independent | Source ↗ |
| Needle-in-a-Haystack | Saturated | 97.7% | 44 | 2026-06-04 | independent | Source ↗ |
| HumanEval+ | Saturated | 90.8% | 25 | 2026-06-04 | independent | Source ↗ |
| CommonsenseQA | Saturated | 90.1% | 28 | 2026-06-04 | independent | Source ↗ |
| MBPP | Saturated | 89.1% | 25 | 2026-06-03 | independent | Source ↗ |
| GSM8K | Saturated | 88.9% | 25 | 2026-06-03 | independent | Source ↗ |
| GLUE | Saturated | 92.7% | 28 | 2026-06-03 | independent | Source ↗ |
| MMLU | Saturated | 90.3% | 25 | 2026-06-02 | independent | Source ↗ |
| HumanEval | Saturated | 95.9% | 25 | 2026-06-02 | independent | Source ↗ |
| ARC-AGI | Saturated | 80.7% | 25 | 2026-06-02 | independent | Source ↗ |
| LiveBench (Continuous) | Active | 77.9% | 94 | 2025-02-01 | independent | Source ↗ |
Local Hardware Execution (Ollama)Hardware Compatible
# 1. Pull and execute model locally with Ollama
ollama run mistral-mistral-medium-3-5
# 2. Or invoke via local OpenAI-compatible endpoint
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-mistral-medium-3-5",
"messages": [{"role": "user", "content": "Analyze reasoning chains on AIME 2026"}]
}'Evaluation & Sourcing Notes
Scores listed for Mistral: Mistral Medium 3.5 represent verified evaluations extracted from official research papers, independent evaluation suites (HELM, LMSYS, OpenCompass, LiveBench), and verified audit reports.
All benchmarks marked as saturated or deprecated reflect historical performance where the benchmark no longer provides active discriminative power.