Model comparison
Gemma 3 27B vs Mistral Large 3
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemma 3 27B #164 (Supported); Mistral Large 3 #113 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 3 27B and Mistral Large 3 share 16 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to Gemma 3 27B; 0 to Mistral Large 3.
Updated July 20, 2026- Shared results
- 16
- Gemma 3 27B only
- 0
- Mistral Large 3 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Gemma 3 27B and Mistral Large 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 16 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.
Mistral Large 3 is priced at $0.50 input / $1.50 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 3 27B. Mistral Large 3 has the larger context window at 128K, compared with 32K for Gemma 3 27B.
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 | Gemma 3 27B | Mistral Large 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 3 27B$0 input / $0 output | Mistral Large 3$0.5 input / $1.5 output | Gemma 3 27B has the lower combined listed price. |
| Generation speedtokens per second | Gemma 3 27B31 tok/s | Mistral Large 348 tok/s | Mistral Large 3 has the higher measured throughput. |
| First-answer latencyseconds to first token | Gemma 3 27B2.04 s | Mistral Large 31.04 s | Mistral Large 3 reaches the first token sooner. |
| Context windowmaximum listed tokens | Gemma 3 27B32K | Mistral Large 3128K | Mistral Large 3 lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Gemma 3 27B | Mistral Large 3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 7.4% | 15.9% | Mistral Large 3 leads |
| AA-GPQA DiamondSource | 42.8% | 68.0% | Mistral Large 3 leads |
| AA-HLESource | 4.7% | 4.1% | Gemma 3 27B leads |
| AA-Omniscience IndexSource | -65.9% | -39.4% | Mistral Large 3 leads |
| AA-Omniscience AccuracySource | 12.5% | 24.1% | Mistral Large 3 leads |
| AA-Omniscience Hallucination RateSource | 89.5% | 83.7% | Mistral Large 3 leads |
Multimodal1 benchmarks
| Benchmark | Gemma 3 27B | Mistral Large 3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 48.0% | 55.7% | Mistral Large 3 leads |
Inst. Following1 benchmarks
| Benchmark | Gemma 3 27B | Mistral Large 3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 31.8% | 36.2% | Mistral Large 3 leads |
Frequently Asked Questions (3)
Can I compare Gemma 3 27B and Mistral Large 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 Gemma 3 27B and Mistral Large 3 today?
Gemma 3 27B: $0.00 input / $0.00 output per 1M tokens Mistral Large 3: $0.50 input / $1.50 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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