Model comparison
Mistral Medium 3.5 128B vs Trinity-Large-Thinking
Head-to-head evidence from 16 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Mistral Medium 3.5 128B unranked (Not scored); Trinity-Large-Thinking #132 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Mistral Medium 3.5 128B and Trinity-Large-Thinking share 16 comparable benchmark results. 0 of 8 categories are comparable. 9 results are unique to Mistral Medium 3.5 128B; 5 to Trinity-Large-Thinking.
Updated July 27, 2026- Shared results
- 16
- Mistral Medium 3.5 128B only
- 9
- Trinity-Large-Thinking only
- 5
- Comparable categories
- 0 / 8
Benchmark data for Mistral Medium 3.5 128B and Trinity-Large-Thinking is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 5 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 Medium 3.5 128B is priced at $1.50 input / $7.50 output per 1M tokens, versus $0.25 input / $0.90 output per 1M tokens for Trinity-Large-Thinking. Trinity-Large-Thinking has the larger context window at 512K, compared with 256K for Mistral Medium 3.5 128B.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Mistral Medium 3.5 128B | Δ | Trinity-Large-Thinking |
|---|---|---|---|
| Coding | Mistral Medium 3.5 128B77.6 | MarginNo overlap | Trinity-Large-ThinkingNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mistral Medium 3.5 128B | Trinity-Large-Thinking | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Trinity-Large-Thinking$0.25 input / $0.9 output | Trinity-Large-Thinking has the lower combined listed price. |
| Generation speedtokens per second | Mistral Medium 3.5 128BNot available | Trinity-Large-ThinkingNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral Medium 3.5 128BNot available | Trinity-Large-ThinkingNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral Medium 3.5 128B256K | Trinity-Large-Thinking512K | Trinity-Large-Thinking lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Trinity-Large-Thinking | Result |
|---|---|---|---|
| τ³-bench resultsSource | 91.4% | — | Not comparable |
| AA Agentic IndexSource | 19.0% | 3.6% | Mistral Medium 3.5 128B leads |
| τ²-bench resultsSource | 94.2% | 90.1% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 21.6% | 3.2% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 933 | 564 | Mistral Medium 3.5 128B leads |
| Gert LabsSource | 39.10% | 32.55% | Mistral Medium 3.5 128B leads |
| AA EnterpriseOps-GymSource | 33.7% | — | Not comparable |
| AA Harvey LABSource | 69.1% | — | Not comparable |
| terminalBenchHardSource | 33.3% | — | Not comparable |
| AA BriefcaseSource | 516 | — | Not comparable |
| AA Tau3 BankingSource | 14.4% | — | Not comparable |
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Trinity-Large-Thinking | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 29.9% | 18.2% | Mistral Medium 3.5 128B leads |
| AA-GPQA DiamondSource | 74.8% | 75.2% | Trinity-Large-Thinking leads |
| AA-HLESource | 12.8% | 14.7% | Trinity-Large-Thinking leads |
| AA-Omniscience IndexSource | -36.3% | -44.2% | Mistral Medium 3.5 128B leads |
| AA-Omniscience AccuracySource | 25.1% | 22.8% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 82.0% | 86.6% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | 33.3% | — | Not comparable |
| GPQA-DSource | — | 76.3% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 83.4% | Not comparable |
Math1 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Trinity-Large-Thinking | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 96.3% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Trinity-Large-Thinking | Result |
|---|---|---|---|
| AA-IFBenchSource | 68.8% | 56.3% | Mistral Medium 3.5 128B leads |
Frequently Asked Questions (3)
Can I compare Mistral Medium 3.5 128B and Trinity-Large-Thinking 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 Mistral Medium 3.5 128B and Trinity-Large-Thinking today?
Mistral Medium 3.5 128B: $1.50 input / $7.50 output per 1M tokens Trinity-Large-Thinking: $0.25 input / $0.90 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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