Model comparison
GPT-5.4 nano vs Ling 2.6 Flash
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: GPT-5.4 nano #25 (Supported); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 nano and Ling 2.6 Flash share 16 comparable benchmark results. 1 of 8 categories are comparable. 13 results are unique to GPT-5.4 nano; 2 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 16
- GPT-5.4 nano only
- 13
- Ling 2.6 Flash only
- 2
- Comparable categories
- 1 / 8
Pick GPT-5.4 nano 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 16 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
GPT-5.4 nano is clearly ahead on the BenchAlign aggregate, 66.79 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 nano 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-5.4 nano gives you the larger context window at 400K, 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-5.4 nano | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Knowledge | GPT-5.4 nano43.8 | Margin→ 15.2 | Ling 2.6 Flash59.0 |
| Agentic | GPT-5.4 nano42.9 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Coding | GPT-5.4 nanoNot measured | MarginNo overlap | Ling 2.6 Flash27.0 |
| Math | GPT-5.4 nano21.0 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Multimodal | GPT-5.4 nano66.1 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Inst. Following | GPT-5.4 nanoNot measured | MarginNo overlap | Ling 2.6 Flash57.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 82.8%B 59%Winner: GPT-5.4 nanoΔ 23.8GPQA: GPT-5.4 nano scored 82.8%; Ling 2.6 Flash scored 59%. GPT-5.4 nano wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 nano | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4 nano$0.2 input / $1.25 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.4 nano191 tok/s | Ling 2.6 Flash209.5 tok/s | Ling 2.6 Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.4 nano3.64 s | Ling 2.6 Flash1.07 s | Ling 2.6 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.4 nano400K | Ling 2.6 Flash262K | GPT-5.4 nano lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | GPT-5.4 nano | Ling 2.6 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46.3% | — | Not comparable |
| OSWorld-VerifiedSource | 39% | — | Not comparable |
| MCP AtlasSource | 56.1% | — | Not comparable |
| ToolathlonSource | 35.5% | — | Not comparable |
| τ²-bench resultsSource | 76% | 86% | Ling 2.6 Flash leads |
| AA Agentic IndexSource | 27.5% | 2.3% | GPT-5.4 nano leads |
| APEX-Agents-AASource | 24.9% | — | Not comparable |
| GDPval-AASource | 30.0% | 2.2% | GPT-5.4 nano leads |
| GDPval-AASource | 1100 | 545 | GPT-5.4 nano leads |
Coding4 benchmarks
Reasoning2 benchmarks
KnowledgeLing 2.6 Flash wins9 benchmarks
| Benchmark | GPT-5.4 nano | Ling 2.6 Flash | Result |
|---|---|---|---|
| GPQASource | 82.8% | 59% | GPT-5.4 nano leads |
| HLESource | 37.7% | — | Not comparable |
| HLE w/o toolsSource | 24.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.2% | 14.1% | GPT-5.4 nano leads |
| AA-GPQA DiamondSource | 81.7% | 59.3% | GPT-5.4 nano leads |
| AA-HLESource | 26.5% | 6.2% | GPT-5.4 nano leads |
| AA-Omniscience IndexSource | -29.5% | -65.7% | GPT-5.4 nano leads |
| AA-Omniscience AccuracySource | 25.4% | 15.4% | GPT-5.4 nano leads |
| AA-Omniscience Hallucination RateSource | 73.6% | 95.8% | GPT-5.4 nano leads |
Math2 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.4 nano or Ling 2.6 Flash?
GPT-5.4 nano is ahead on BenchLM's BenchAlign leaderboard, 66.79 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 82.8% and 59%.
Which is better for knowledge tasks, GPT-5.4 nano or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 43.8. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
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