Model comparison
DeepSeek V4 Flash 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: DeepSeek V4 Flash #61 (Estimated); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash and Ling 2.6 Flash share 1 comparable benchmark result. 2 of 8 categories are comparable. 21 results are unique to DeepSeek V4 Flash; 17 to Ling 2.6 Flash.
Updated July 23, 2026- Shared results
- 1
- DeepSeek V4 Flash only
- 21
- Ling 2.6 Flash only
- 17
- Comparable categories
- 2 / 8
Pick DeepSeek V4 Flash if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Flash is clearly ahead on the BenchAlign aggregate, 58.88 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Flash's sharpest advantage is in coding, where it averages 64.2 against 27. The single biggest benchmark swing on the page is GPQA, 71.2% to 59%. Ling 2.6 Flash does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
DeepSeek V4 Flash 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 | DeepSeek V4 Flash | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Coding | DeepSeek V4 Flash64.2 | Margin← 37.2 | Ling 2.6 Flash27.0 |
| Knowledge | DeepSeek V4 Flash38.8 | Margin→ 20.2 | Ling 2.6 Flash59.0 |
| Agentic | DeepSeek V4 Flash49.1 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Math | DeepSeek V4 Flash40.8 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Inst. Following | DeepSeek V4 FlashNot 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 71.2%B 59%Winner: DeepSeek V4 FlashΔ 12.2GPQA: DeepSeek V4 Flash scored 71.2%; Ling 2.6 Flash scored 59%. DeepSeek V4 Flash wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash$0.14 input / $0.28 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V4 FlashNot available | Ling 2.6 Flash209.5 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 FlashNot available | Ling 2.6 Flash1.07 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash1M | Ling 2.6 Flash262K | DeepSeek V4 Flash lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | DeepSeek V4 Flash | Ling 2.6 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 49.1% | — | Not comparable |
| MCP AtlasSource | 64% | — | Not comparable |
| ToolathlonSource | 40.7% | — | Not comparable |
| Claw-EvalSource | 57.8% | — | Not comparable |
| Gert LabsSource | 54.35% | — | Not comparable |
| τ²-bench resultsSource | — | 86% | Not comparable |
| GDPval-AASource | — | 2.2% | Not comparable |
| GDPval-AASource | — | 545 | Not comparable |
| AA Agentic IndexSource | — | 2.3% | Not comparable |
CodingDeepSeek V4 Flash wins7 benchmarks
| Benchmark | DeepSeek V4 Flash | Ling 2.6 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.7% | — | Not comparable |
| SWE-bench ProSource | 49.1% | — | Not comparable |
| SWE MultilingualSource | 69.7% | — | Not comparable |
| Terminal-Bench 2.0Source | 49.1% | — | Not comparable |
| SciCodeSource | — | 27% | Not comparable |
| AA Coding IndexSource | — | 25.3% | Not comparable |
| AA-SciCodeSource | — | 27.1% | Not comparable |
Reasoning4 benchmarks
KnowledgeLing 2.6 Flash wins12 benchmarks
| Benchmark | DeepSeek V4 Flash | Ling 2.6 Flash | Result |
|---|---|---|---|
| MMLU-ProSource | 83% | — | Not comparable |
| SimpleQASource | 23.1% | — | Not comparable |
| Chinese-SimpleQASource | 71.5% | — | Not comparable |
| GPQASource | 71.2% | 59% | DeepSeek V4 Flash leads |
| GPQA-DSource | 71.2% | — | Not comparable |
| HLESource | 8.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 14.1% | 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 |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Flash | Ling 2.6 Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Flash or Ling 2.6 Flash?
DeepSeek V4 Flash is ahead on BenchLM's BenchAlign leaderboard, 58.88 to 43.87. The biggest single separator in this matchup is GPQA, where the scores are 71.2% and 59%.
Which is better for knowledge tasks, DeepSeek V4 Flash or Ling 2.6 Flash?
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 38.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Flash or Ling 2.6 Flash?
DeepSeek V4 Flash has the edge for coding in this comparison, averaging 64.2 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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