Model comparison
GPT-5.5 Pro vs Ling 2.6 Flash
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 Pro #38 (Estimated); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 Pro and Ling 2.6 Flash share 1 comparable benchmark result. 1 of 8 categories are comparable. 6 results are unique to GPT-5.5 Pro; 17 to Ling 2.6 Flash.
Updated July 22, 2026- Shared results
- 1
- GPT-5.5 Pro only
- 6
- Ling 2.6 Flash only
- 17
- Comparable categories
- 1 / 8
Pick GPT-5.5 Pro if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.5 Pro is clearly ahead on the BenchAlign aggregate, 63.69 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5 Pro 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. GPT-5.5 Pro gives you the larger context window at 1M, compared with 262K for Ling 2.6 Flash.
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 | GPT-5.5 Pro | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Knowledge | GPT-5.5 Pro57.2 | Margin→ 1.8 | Ling 2.6 Flash59.0 |
| Agentic | GPT-5.5 Pro90.1 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Coding | GPT-5.5 ProNot measured | MarginNo overlap | Ling 2.6 Flash27.0 |
| Math | GPT-5.5 Pro48.1 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Inst. Following | GPT-5.5 ProNot measured | MarginNo overlap | Ling 2.6 Flash57.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 Pro | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5 Pro$30 input / $180 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.5 ProNot available | Ling 2.6 Flash209.5 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5 ProNot available | Ling 2.6 Flash1.07 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.5 Pro1M | Ling 2.6 Flash262K | GPT-5.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
KnowledgeLing 2.6 Flash wins9 benchmarks
| Benchmark | GPT-5.5 Pro | Ling 2.6 Flash | Result |
|---|---|---|---|
| HLESource | 57.2% | — | Not comparable |
| HLE w/o toolsSource | 43.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 14.1% | Not comparable |
| GPQASource | — | 59% | Not comparable |
| AA-GPQA DiamondSource | — | 59.3% | Not comparable |
| AA-HLESource | — | 6.2% | Not comparable |
| AA-Omniscience IndexSource | — | -65.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 15.4% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 95.8% | Not comparable |
Math3 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.5 Pro or Ling 2.6 Flash?
GPT-5.5 Pro is ahead on BenchLM's BenchAlign leaderboard, 63.69 to 43.87.
Which is better for knowledge tasks, GPT-5.5 Pro or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 57.2. GPT-5.5 Pro stays close enough that the answer can still flip depending on your workload.
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