Model comparison
GPT-4.1 vs Ling 2.6 Flash
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4.1 #108 (Supported); 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 and Ling 2.6 Flash share 12 comparable benchmark results. 3 of 8 categories are comparable. 8 results are unique to GPT-4.1; 6 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 12
- GPT-4.1 only
- 8
- Ling 2.6 Flash only
- 6
- Comparable categories
- 3 / 8
Pick GPT-4.1 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-4.1 is clearly ahead on the BenchAlign aggregate, 51.11 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in instruction following, where it averages 87.4 against 57. The single biggest benchmark swing on the page is GPQA, 66.3% to 59%.
GPT-4.1 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 | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Inst. Following | GPT-4.187.4 | Margin← 30.4 | Ling 2.6 Flash57.0 |
| Coding | GPT-4.154.6 | Margin← 27.6 | Ling 2.6 Flash27.0 |
| Knowledge | GPT-4.166.3 | Margin← 7.3 | Ling 2.6 Flash59.0 |
| Math | GPT-4.14.1 | 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 66.3%B 59%Winner: GPT-4.1Δ 7.3GPQA: GPT-4.1 scored 66.3%; Ling 2.6 Flash scored 59%. GPT-4.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1$2 input / $8 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-4.1108 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-4.11.02 s | Ling 2.6 Flash1.07 s | GPT-4.1 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-4.11M | Ling 2.6 Flash262K | GPT-4.1 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
CodingGPT-4.1 wins4 benchmarks
Reasoning2 benchmarks
KnowledgeGPT-4.1 wins8 benchmarks
| Benchmark | GPT-4.1 | Ling 2.6 Flash | Result |
|---|---|---|---|
| MMLUSource | 90.2% | — | Not comparable |
| GPQASource | 66.3% | 59% | GPT-4.1 leads |
| Artificial Analysis Intelligence IndexSource | 19.4% | 14.1% | GPT-4.1 leads |
| AA-GPQA DiamondSource | 66.6% | 59.3% | GPT-4.1 leads |
| AA-HLESource | 4.6% | 6.2% | Ling 2.6 Flash leads |
| AA-Omniscience IndexSource | -36.2% | -65.7% | GPT-4.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 15.4% | GPT-4.1 leads |
| AA-Omniscience Hallucination RateSource | 79.6% | 95.8% | GPT-4.1 leads |
Math2 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (4)
Which is better, GPT-4.1 or Ling 2.6 Flash?
GPT-4.1 is ahead on BenchLM's BenchAlign leaderboard, 51.11 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 66.3% and 59%.
Which is better for knowledge tasks, GPT-4.1 or Ling 2.6 Flash?
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 59. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-4.1 or Ling 2.6 Flash?
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 27. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for instruction following, GPT-4.1 or Ling 2.6 Flash?
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 57. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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