Model comparison
GPT-4.1 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-4.1 nano #161 (Estimated); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and Ling 2.6 Flash share 16 comparable benchmark results. 2 of 8 categories are comparable. 5 results are unique to GPT-4.1 nano; 2 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 16
- GPT-4.1 nano only
- 5
- Ling 2.6 Flash only
- 2
- Comparable categories
- 2 / 8
Pick Ling 2.6 Flash if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 5 evidence categories; 2 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 has the cleaner BenchAlign overall profile here, landing at 43.87 versus 42.06. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Ling 2.6 Flash's sharpest advantage is in knowledge, where it averages 59 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 59%. GPT-4.1 nano does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano 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-4.1 nano | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Inst. Following | GPT-4.1 nano83.2 | Margin← 26.2 | Ling 2.6 Flash57.0 |
| Knowledge | GPT-4.1 nano50.3 | Margin→ 8.7 | Ling 2.6 Flash59.0 |
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | Ling 2.6 Flash27.0 |
| Math | GPT-4.1 nano1.0 | MarginNo overlap | Ling 2.6 FlashNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 50.3%B 59%Winner: Ling 2.6 FlashΔ 8.7GPQA: GPT-4.1 nano scored 50.3%; Ling 2.6 Flash scored 59%. Ling 2.6 Flash wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 nano | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-4.1 nano181 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-4.1 nano0.63 s | Ling 2.6 Flash1.07 s | GPT-4.1 nano reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Ling 2.6 Flash262K | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
KnowledgeLing 2.6 Flash wins8 benchmarks
| Benchmark | GPT-4.1 nano | Ling 2.6 Flash | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | 59% | Ling 2.6 Flash leads |
| Artificial Analysis Intelligence IndexSource | 9.6% | 14.1% | Ling 2.6 Flash leads |
| AA-GPQA DiamondSource | 51.2% | 59.3% | Ling 2.6 Flash leads |
| AA-HLESource | 3.9% | 6.2% | Ling 2.6 Flash leads |
| AA-Omniscience IndexSource | -56.4% | -65.7% | GPT-4.1 nano leads |
| AA-Omniscience AccuracySource | 13.3% | 15.4% | Ling 2.6 Flash leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 95.8% | GPT-4.1 nano leads |
Math1 benchmarks
| Benchmark | GPT-4.1 nano | Ling 2.6 Flash | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.034% | — | Not comparable |
Multimodal2 benchmarks
Frequently Asked Questions (3)
Which is better, GPT-4.1 nano or Ling 2.6 Flash?
Ling 2.6 Flash is ahead on BenchLM's BenchAlign leaderboard, 43.87 to 42.06. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 59%.
Which is better for knowledge tasks, GPT-4.1 nano or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 50.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for instruction following, GPT-4.1 nano or Ling 2.6 Flash?
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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