Model comparison
LFM2.5-VL-1.6B-Extract vs Mistral Medium 3
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: LFM2.5-VL-1.6B-Extract unranked (Not scored); Mistral Medium 3 #156 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-1.6B-Extract and Mistral Medium 3 share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-1.6B-Extract; 1 to Mistral Medium 3.
Updated July 23, 2026- Shared results
- 12
- LFM2.5-VL-1.6B-Extract only
- 3
- Mistral Medium 3 only
- 1
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-VL-1.6B-Extract and Mistral Medium 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-VL-1.6B-Extract | Mistral Medium 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-1.6B-ExtractNot available | Mistral Medium 3$0.4 input / $2 output | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-1.6B-ExtractNot available | Mistral Medium 357 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-1.6B-ExtractNot available | Mistral Medium 31.20 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-1.6B-Extract128K | Mistral Medium 3128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Mistral Medium 3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 8.5% | 24.3% | Mistral Medium 3 leads |
Coding1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Mistral Medium 3 | Result |
|---|---|---|---|
| AA-SciCodeSource | 3.0% | 33.1% | Mistral Medium 3 leads |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Mistral Medium 3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 1.0% | 12.5% | Mistral Medium 3 leads |
| AA-GPQA DiamondSource | 28.9% | 57.8% | Mistral Medium 3 leads |
| AA-HLESource | 5.1% | 4.3% | LFM2.5-VL-1.6B-Extract leads |
| AA-Omniscience IndexSource | -83.9% | -31.5% | Mistral Medium 3 leads |
| AA-Omniscience AccuracySource | 5.2% | 18.3% | Mistral Medium 3 leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 60.9% | Mistral Medium 3 leads |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Mistral Medium 3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 33.1% | 39.3% | Mistral Medium 3 leads |
Frequently Asked Questions (3)
Can I compare LFM2.5-VL-1.6B-Extract and Mistral Medium 3 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for LFM2.5-VL-1.6B-Extract and Mistral Medium 3 today?
Mistral Medium 3: $0.40 input / $2.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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