Model comparison
Llama 4 Behemoth vs Mistral Medium 3.5 128B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Llama 4 Behemoth #185 (Estimated); Mistral Medium 3.5 128B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Llama 4 Behemoth and Mistral Medium 3.5 128B share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to Llama 4 Behemoth; 25 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 0
- Llama 4 Behemoth only
- 0
- Mistral Medium 3.5 128B only
- 25
- Comparable categories
- 0 / 8
Benchmark data for Llama 4 Behemoth and Mistral Medium 3.5 128B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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 does not have sourced benchmark coverage for Llama 4 Behemoth yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Mistral Medium 3.5 128B is priced at $1.50 input / $7.50 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Behemoth. Mistral Medium 3.5 128B has the larger context window at 256K, compared with 32K for Llama 4 Behemoth.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 4 Behemoth | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 4 Behemoth$0 input / $0 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Llama 4 Behemoth has the lower combined listed price. |
| Generation speedtokens per second | Llama 4 BehemothNot available | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Llama 4 BehemothNot available | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Llama 4 Behemoth32K | Mistral Medium 3.5 128B256K | Mistral Medium 3.5 128B lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Llama 4 Behemoth | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| AA Agentic IndexSource | — | 19.0% | Not comparable |
| τ²-bench resultsSource | — | 94.2% | Not comparable |
| GDPval-AASource | — | 21.6% | Not comparable |
| GDPval-AASource | — | 933 | Not comparable |
| Gert LabsSource | — | 39.10% | Not comparable |
| 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 |
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | Llama 4 Behemoth | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | — | 29.9% | Not comparable |
| AA-GPQA DiamondSource | — | 74.8% | Not comparable |
| AA-HLESource | — | 12.8% | Not comparable |
| AA-Omniscience IndexSource | — | -36.3% | Not comparable |
| AA-Omniscience AccuracySource | — | 25.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 82.0% | Not comparable |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Llama 4 Behemoth | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | — | 64.9% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Llama 4 Behemoth | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 68.8% | Not comparable |
Frequently Asked Questions (3)
Can I compare Llama 4 Behemoth and Mistral Medium 3.5 128B 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 Llama 4 Behemoth and Mistral Medium 3.5 128B today?
Llama 4 Behemoth: $0.00 input / $0.00 output per 1M tokens Mistral Medium 3.5 128B: $1.50 input / $7.50 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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