# GPT-5.2 vs Qwen3.8-Flash-Next

> Side-by-side benchmark comparison for GPT-5.2 and Qwen3.8-Flash-Next across agentic, coding, multimodal, knowledge, reasoning, multilingual, and math tasks.

- Canonical page: https://benchlm.ai/compare/gpt-5-2-vs-qwen3-8-flash-next
- Last updated: September 18, 2026

- Shared sourced benchmarks: 5
- HTML indexing: indexable
- Ranking lane: BenchAlign v5

## Quick Verdict

Pick GPT-5.2 if you want the stronger benchmark profile. Qwen3.8-Flash-Next only makes more sense when its price, context window, or workload-specific category wins matter more than the overall score.

## Summary

- GPT-5.2 leads overall 64.86 to 56.98.
- The clearest category separation is in reasoning, where the averages are 53.7 for GPT-5.2 and 75.8 for Qwen3.8-Flash-Next.
- The biggest single benchmark swing is JobBench in Agentic, with scores of 34.3% and 55.7%.
- Qwen3.8-Flash-Next is the cheaper option on output tokens, which matters if you expect large responses or heavy interactive use.
- GPT-5.2 also has the larger context window at 400K.

## Model Snapshot

| Property | GPT-5.2 | Qwen3.8-Flash-Next |
|----------|----------|----------|
| Creator | OpenAI | Alibaba |
| Type | Proprietary | Open Weight |
| Reasoning | Reasoning | Reasoning |
| Context | 400K | 262K |
| Overall Score | 64.86 | 56.98 |
| Benchmarks Covered | 15 | 24 |
| Pricing (input/output) | $1.75 / $14.00 | $0.00 / $0.00 |

## Category Breakdown

### Agentic

- Winner: Coming soon
- GPT-5.2 public-lane score: 42.7 (Supported · #107/154)
- Qwen3.8-Flash-Next public-lane score: 58.8 (Estimated · #29/154)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| BrowseComp | 65.8% | Coming soon | Coming soon |
| OSWorld-Verified | 47.3% | Coming soon | Coming soon |
| Gert Labs | 46.54% | Coming soon | Coming soon |
| JobBench | 34.3% | 55.7% | Qwen3.8-Flash-Next |
| CoWorkBench | Coming soon | 73.9% | Coming soon |
| Agents' Last Exam | Coming soon | 51.2% | Coming soon |
| Toolathlon-Verified | Coming soon | 73.5% | Coming soon |
| AndroidWorld | Coming soon | 84.5% | Coming soon |
| OSWorld 2.0 | Coming soon | 19.4% | Coming soon |

### Coding

- Winner: Qwen3.8-Flash-Next
- GPT-5.2 public-lane score: 46.3 (Supported · #87/154)
- Qwen3.8-Flash-Next public-lane score: 57.9 (Supported · #30/154)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| SWE-bench Verified | 80% | Coming soon | Coming soon |
| SWE-bench Pro | 55.6% | 62.5% | Qwen3.8-Flash-Next |
| Vibe Code Bench | 53.50% | Coming soon | Coming soon |
| SWE Multilingual | Coming soon | 81% | Coming soon |
| NL2Repo | Coming soon | 48.1% | Coming soon |
| DeepSWE | Coming soon | 58.7% | Coming soon |
| LiveCodeBench v6 | Coming soon | 91.9% | Coming soon |

### Multimodal & Grounded

- Winner: Coming soon
- GPT-5.2 public-lane score: 66.3 (#22/48)
- Qwen3.8-Flash-Next public-lane score: 83.1 (#7/48)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| MMMU-Pro | 79.5% | Coming soon | Coming soon |
| MathVision | 83.0% | 90.6% | Qwen3.8-Flash-Next |
| CharXiv | 82.1% | 90.6% | Qwen3.8-Flash-Next |
| V* | 75.9% | Coming soon | Coming soon |
| Vision2Web | Coming soon | 64.0% | Coming soon |
| ERQA | Coming soon | 72.3% | Coming soon |
| LVBench | Coming soon | 76.6% | Coming soon |
| RealWorldQA | Coming soon | 88.5% | Coming soon |
| MathVision w/ Python | Coming soon | 95.7% | Coming soon |
| CharXiv w/o tools | Coming soon | 84.6% | Coming soon |

### Reasoning

- Winner: Coming soon
- GPT-5.2 public-lane score: 53.7 (Unranked · 3 rankable rows)
- Qwen3.8-Flash-Next public-lane score: 75.8 (Unranked · 2 rankable rows)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| ARC-AGI-2 | 52.9% | Coming soon | Coming soon |

### Knowledge

- Winner: GPT-5.2
- GPT-5.2 public-lane score: 61.6 (Supported · #29/184)
- Qwen3.8-Flash-Next public-lane score: 55.6 (Supported · #53/184)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| GPQA | 92.4% | 91.7% | GPT-5.2 |
| GPQA-D | Coming soon | 91.7% | Coming soon |
| HLE | Coming soon | 35.9% | Coming soon |
| HLE w/o tools | Coming soon | 35.9% | Coming soon |

### Instruction Following

- Winner: Coming soon
- GPT-5.2 public-lane score: 91.2 (#14/124)
- Qwen3.8-Flash-Next public-lane score: 87.2 (#33/124)

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| IFBench | Coming soon | 81.3% | Coming soon |

### Mathematics

- Winner: Coming soon
- GPT-5.2 public-lane score: 57.4 (Unranked · 2 rankable rows)
- Qwen3.8-Flash-Next public-lane score: Coming soon

| Benchmark | GPT-5.2 | Qwen3.8-Flash-Next | Winner |
|-----------|-----------|-----------|--------|
| FrontierMath v2 (Tiers 1-3) | 40.700% | Coming soon | Coming soon |
| FrontierMath v2 (Tier 4) | 18.800% | Coming soon | Coming soon |

## FAQ

### Which is better overall, GPT-5.2 or Qwen3.8-Flash-Next?

GPT-5.2 is ahead overall on BenchLM right now.

### Where is the biggest gap between GPT-5.2 and Qwen3.8-Flash-Next?

The widest category gap is in reasoning, where the averages are 53.7 for GPT-5.2 and 75.8 for Qwen3.8-Flash-Next.

### How many benchmarks does GPT-5.2 cover on BenchLM?

GPT-5.2 currently has 15 sourced benchmark scores on BenchLM.

### How many benchmarks does Qwen3.8-Flash-Next cover on BenchLM?

Qwen3.8-Flash-Next currently has 24 sourced benchmark scores on BenchLM.

## Related Comparisons

- [GPT-5.2 vs GPT-5.2 Pro](/compare/gpt-5-2-vs-gpt-5-2-pro)
- [Qwen3.8-Flash-Next vs GPT-5.2 Pro](/compare/gpt-5-2-pro-vs-qwen3-8-flash-next)
- [GPT-5.2 vs GPT-5.2 Instant](/compare/gpt-5-2-vs-gpt-5-2-instant)
- [Qwen3.8-Flash-Next vs GPT-5.2 Instant](/compare/gpt-5-2-instant-vs-qwen3-8-flash-next)

## Explore More

- [GPT-5.2 profile](/models/gpt-5-2)
- [Qwen3.8-Flash-Next profile](/models/qwen3-8-flash-next)
- [Compare Pricing](/llm-pricing)
- [Alternative Finder](/tools/alternative-finder)
- [LLM Selector](/tools/llm-selector)
- [Overall Rankings](/best/overall)