Model comparison
Ling 2.6 Flash vs Mistral Medium 3.5 128B
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Ling 2.6 Flash #162 (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. Ling 2.6 Flash and Mistral Medium 3.5 128B share 15 comparable benchmark results. 1 of 8 categories are comparable. 3 results are unique to Ling 2.6 Flash; 10 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 15
- Ling 2.6 Flash only
- 3
- Mistral Medium 3.5 128B only
- 10
- Comparable categories
- 1 / 8
Treat this as a split decision. Ling 2.6 Flash makes more sense if you need the larger 262K context window or you would rather avoid the extra latency and token burn of a reasoning model; Mistral Medium 3.5 128B is the better fit if coding is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Ling 2.6 Flash and Mistral Medium 3.5 128B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Mistral Medium 3.5 128B is the reasoning model in the pair, while Ling 2.6 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Ling 2.6 Flash gives you the larger context window at 262K, 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 | Ling 2.6 Flash | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | Ling 2.6 Flash27.0 | Margin→ 50.6 | Mistral Medium 3.5 128B77.6 |
| Knowledge | Ling 2.6 Flash59.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Inst. Following | Ling 2.6 Flash57.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Ling 2.6 Flash | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Ling 2.6 FlashNot available | Mistral Medium 3.5 128B$1.5 input / $7.5 output | A complete price comparison is not available. |
| Generation speedtokens per second | Ling 2.6 Flash209.5 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Ling 2.6 Flash1.07 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Ling 2.6 Flash262K | Mistral Medium 3.5 128B256K | Ling 2.6 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Ling 2.6 Flash | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| τ²-bench resultsSource | 86% | 94.2% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 2.5% | 21.6% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 550 | 933 | Mistral Medium 3.5 128B leads |
| AA Agentic IndexSource | 2.3% | 19.0% | Mistral Medium 3.5 128B leads |
| τ³-bench resultsSource | — | 91.4% | 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 |
CodingMistral Medium 3.5 128B wins4 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Ling 2.6 Flash | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.1% | 29.9% | Mistral Medium 3.5 128B leads |
| GPQASource | 59% | — | Not comparable |
| AA-GPQA DiamondSource | 59.3% | 74.8% | Mistral Medium 3.5 128B leads |
| AA-HLESource | 6.2% | 12.8% | Mistral Medium 3.5 128B leads |
| AA-Omniscience IndexSource | -65.7% | -36.3% | Mistral Medium 3.5 128B leads |
| AA-Omniscience AccuracySource | 15.4% | 25.1% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Ling 2.6 Flash | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | — | 64.9% | Not comparable |
Frequently Asked Questions (2)
Which is better, Ling 2.6 Flash or Mistral Medium 3.5 128B?
Ling 2.6 Flash and Mistral Medium 3.5 128B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, Ling 2.6 Flash or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 27. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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