GPT-5.5 vs Qwen3.8-Flash-Next
Full profiles: GPT-5.5 (OpenAI) and Qwen3.8-Flash-Next (Alibaba): every benchmark, price and rank.
Updated · Data from 13 sources
As of 6 Nov 2026, GPT-5.5 and Qwen3.8-Flash-Next are neck and neck on quality (64.3 vs 54.6 on the BenchLeader Index, inside the ±10.3 margin), and Qwen3.8-Flash-Next is 49× cheaper at $0.230 vs $11.25 per 1M tokens blended. Choose GPT-5.5 for knowledge (64 vs 38), reasoning (69 vs 45), and long context (67 vs 65) and fast first answers (58.0 s vs 125.1 s), where it also streams ~1.5× faster; choose Qwen3.8-Flash-Next for coding (66 vs 61) and agents & tools (64 vs 62).
Key facts (as of 6 Nov 2026)
- GPT-5.5 scores 64.3 on the BenchLeader Index and Qwen3.8-Flash-Next scores 54.6 (BenchLeader, 6 Nov 2026).
- GPT-5.5 costs $11.25 per 1M tokens blended (list price, 6 Nov 2026).
- Qwen3.8-Flash-Next costs $0.230 per 1M tokens blended (list price, 6 Nov 2026).
- GPT-5.5 streams ~1.5× faster than Qwen3.8-Flash-Next (Artificial Analysis, 7-day median, 6 Nov 2026).
- Of the 23 benchmarks both models have, GPT-5.5 leads 14 (BenchLeader, 6 Nov 2026).
- On Arena WebDev, GPT-5.5 is rated 1512 and Qwen3.8-Flash-Next 1628 (LMArena, as published by 6 Nov 2026).
GPT-5.5 benchmarks 
Qwen3.8-Flash-Next benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Qwen3.8-Flash-Next | 66 vs 61 category score; Arena WebDev 1628 vs 1512 | Medium |
| Agents & tool use | Qwen3.8-Flash-Next | 64 vs 62 category score; τ³-Banking (AA) 45.4% vs 39.0% | Medium |
| Reasoning | GPT-5.5 | 69 vs 45 category score; LiveBench 1.0 Reasoning 89.7% vs 87.4% | High |
| Knowledge | GPT-5.5 | 64 vs 38 category score; LiveBench 1.0 Data Analysis 81.6% vs 74.2% | High |
| Long documents | GPT-5.5 | 67 vs 65 category score; AA-LCR v1.1 84.3% vs 79.7% | Medium |
| Images and other media | Either | 62 vs 62 category score; MMMU-Pro 79.9% vs 79.8% | Either |
| Lowest cost | Qwen3.8-Flash-Next | $0.230 vs $11.25 per 1M blended; agentic coding session $0.011 vs $0.420 | – |
| Fastest first answer | GPT-5.5 | 58.0 s (GPT-5.5 (xhigh)) vs 125.1 s (Qwen3.8-Flash-Next) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-5.5 (xhigh) and Qwen3.8-Flash-Next are neck and neck on quality (64.3 vs 54.6, within the ±10.3 margin).
- GPT-5.5 (xhigh) is stronger in instruction following, knowledge, long context, maths, multimodal, reasoning.
- Qwen3.8-Flash-Next is stronger in agents & tools, coding.
- Qwen3.8-Flash-Next is 49× cheaper ($0.230 vs $11.25 per 1M blended).
At a glance
| Spec | GPT-5.5 | Qwen3.8-Flash-Next |
|---|---|---|
| Developer | OpenAI | Alibaba |
| Released | 23 Apr 2026 | 26 Aug 2026 |
| Context window (tokens) | 1,050,000 | 256,000 |
| Compared at | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $5.00 / $30.00 / $0.500 | $0.150 / $0.470 / – |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next | Leader |
|---|---|---|---|
| Reasoning | 68.9 | 44.5 | GPT-5.5 |
| Coding | 60.9 | 65.7 | Qwen3.8-Flash-Next |
| Agents & tools | 61.7 | 64.1 | Qwen3.8-Flash-Next |
| Maths | 69.6 | – | not comparable |
| Knowledge | 64.0 | 38.4 | GPT-5.5 |
| Instruction following | 73.9 | – | not comparable |
| Multimodal | 62.4 | 62.2 | level |
| Long context | 67.1 | 64.6 | GPT-5.5 |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Maths, Instruction following, Human preference.
ModelIndex
Where sources disagree
Across the 23 benchmarks both have, GPT-5.5 leads 14, Qwen3.8-Flash-Next leads 9.
| Overall index | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next | Leader |
|---|---|---|---|
| BenchLeader Index | 64.3 | 54.6 | neck and neck |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next |
|---|---|---|
| Blended price per 1M tokens | $11.25 | $0.230 |
| Input / output per 1M | $5.00 / $30.00 | $0.150 / $0.470 |
| Cached input per 1M | $0.500 | – |
| Price per task: chat reply | $0.011 | $0.0002 |
| Price per task: summarize a 30-page report | $0.078 | $0.0021 |
| Price per task: code edit | $0.075 | $0.0016 |
| Price per task: agentic coding session | $0.420 | $0.011 |
| Price per task: structured extraction | $0.016 | $0.0004 |
| Agentic coding session, input mostly cached | $0.217 | – |
| Output speed | 79 tok/s | 64 tok/s |
| Time to first answer | 58.0 s | 125.1 s |
| Time to a complete answer | 115.5 s | 286.8 s |
Each model is compared at its best-ranked reasoning-effort setting. GPT-5.5 (xhigh) scores 64.3 and takes 58 s to its first answer token; GPT-5.5 (high) scores 63.7 in 27 s, GPT-5.5 scores 61.7 in 2 s. Qwen3.8-Flash-Next scores 54.6 and takes 125 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
131 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 64.3 | 54.6 |
| Agents & tools score | BenchLeader | 61.7 | 64.1 |
| Coding score | BenchLeader | 60.9 | 65.7 |
| Instruction following score | BenchLeader | 73.9 | – |
| Knowledge score | BenchLeader | 64.0 | 38.4 |
| Long context score | BenchLeader | 67.1 | 64.6 |
| Maths score | BenchLeader | 69.6 | – |
| Multimodal score | BenchLeader | 62.4 | 62.2 |
| Reasoning score | BenchLeader | 68.9 | 44.5 |
| Blended price $/M | BenchLeader | $11.25 | $0.230 |
| Output speed | BenchLeader | 79 tok/s | 64 tok/s |
| Time to first answer | BenchLeader | 58.0 s | 125.1 s |
| Context window | BenchLeader | 1,050,000 tokens | 256,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.7%† | – |
| SimpleBench | SimpleBench | 69.0%† | – |
| Arena Hard Prompts | Arena (LMArena) | 1499† | – |
| LiveBench 1.0 Reasoning | LiveBench | 89.7% | 87.4% |
| GPQA Diamond (AA) | Artificial Analysis | 93.5% | 92.3% |
| Humanity's Last Exam (AA) | Artificial Analysis | 45.8% | 38.0% |
| HLE-Diamond (AA) | Artificial Analysis | – | 16.3% |
| GPQA Diamond (Vals) | Vals AI | 93.2% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 88.8%† | – |
| ARC-AGI-1 | ARC Prize | 95.0% | – |
| ARC-AGI-2 | ARC Prize | 85.0% | – |
| ARC-AGI-3 | ARC Prize | 0.4%† | – |
| CritPt | Artificial Analysis | 27.1% | 11.1% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 63.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 56.0% | – |
| CL-bench Life | Epoch AI Benchmarking Hub | 22.2%† | – |
| LMCA | Epoch AI Benchmarking Hub | 54.3% | – |
| DTBench | Epoch AI Benchmarking Hub | 96.0% | – |
| ForecastBench | ForecastBench | 64.5%† | – |
| BTF-3 | Epoch AI Benchmarking Hub | 14.3%† | – |
| Coding | |||
| SciCode | SciCode | 55.8% | – |
| WeirdML | WeirdML | 84.9% | – |
| FrontierCode | Cognition | 43.0%† | – |
| GSO-Bench | GSO-Bench | 40.2% | – |
| Arena Coding | Arena (LMArena) | 1510† | – |
Show all 131 benchmarks
| Metric | Publisher | GPT-5.5 (xhigh) | Qwen3.8-Flash-Next |
|---|---|---|---|
| Coding | |||
| Arena WebDev | Arena (LMArena) | 1512 | 1628 |
| LiveBench 1.0 Coding | LiveBench | 82.2% | 72.5% |
| SciCode (AA) | Artificial Analysis | 55.8% | 50.6% |
| LiveCodeBench | Vals AI | 85.3% | – |
| SWE-bench (Vals) | Vals AI | 82.6% | – |
| Code Migration | Vals AI | 45.2% | – |
| ProgramBench | Vals AI | 0.5% | – |
| Vibe Code Bench v1.1 | Vals AI | 69.8% | – |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 45.4% | – |
| SWE Atlas: Refactoring | Scale AI SEAL | 44.8% | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 42.6% | – |
| MirrorCode | Epoch AI Benchmarking Hub | 10.0%† | – |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | 88.1%† | – |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 67.0% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1943.0 | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 14.7% | 25.3% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 84.3% | 86.1% |
| GameBench 2 Overall | DuelLab GameBench 2 | 49.0† | – |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | 59.7 | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 84.7%† | – |
| OSWorld-Verified 2.0 | OSWorld | 13.0% | – |
| Remote Labor Index | Scale AI / CAIS | 6.3%† | – |
| APEX-Agents | Mercor | 55.1%† | – |
| Arena Agent | Arena (LMArena) | – | -4.4 |
| LiveBench Agentic Coding | LiveBench | 54.0% | 61.6% |
| Terminal-Bench Hard | Artificial Analysis | 60.6% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 93.9% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 76.4%† | – |
| MCP Atlas | Scale AI SEAL | 75.3% | – |
| HiL-Bench | Scale AI SEAL | 39.7%† | – |
| τ³-bench Banking | τ²-bench | 44.6% | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 56.4% |
| GDPval-AA v2.1 | Artificial Analysis | – | 1636 |
| τ³-Banking (AA) | Artificial Analysis | 39.0% | 45.4% |
| ITBench SRE (AA) | Artificial Analysis | 45.8% | – |
| Analyst Agent (AA) | Artificial Analysis | 50.0% | – |
| APEX-Agents (AA) | Artificial Analysis | 37.7% | – |
| Finance Agent v2 | Vals AI | 51.8% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 3.8% | – |
| Legal Research Bench | Vals AI | 40.4% | – |
| SkillsBench | Vals AI | 62.2% | – |
| SREBench | Vals AI | 3.8% | – |
| Tax Agent Bench | Vals AI | 21.8% | – |
| Terminal-Bench 2.0 (Vals) | Vals AI | 73.2%† | – |
| Time Horizon Index: KSP | Vals AI | 8.3% | – |
| DeepResearch Bench | DeepResearch Bench | 54.0%† | – |
| PostTrainBench | Epoch AI Benchmarking Hub | 27.2% | – |
| ExploitBench | Epoch AI Benchmarking Hub | 47.4%† | – |
| GBAEval | Epoch AI Benchmarking Hub | 53.2%† | – |
| Vending-Bench 2 | Andon Labs | 7523.8† | – |
| AutomationBench | Artificial Analysis | 47.3% | 55.9% |
| GDP.pdf (AA) | Artificial Analysis | 21.2% | 15.6% |
| EnterpriseOps-Gym | Artificial Analysis | 46.6% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1135 | 1574 |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 85.3% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 72.5% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 84.4%† | – |
| ProofBench | Vals AI | 50.0% | – |
| LiveBench Mathematics | LiveBench | 95.9% | 85.8% |
| AIME 2026 | MathArena | 100.0% | – |
| HMMT February 2026 | MathArena | 98.5% | – |
| USAMO 2026 | MathArena | 98.2% | – |
| MathArena Apex | MathArena | 80.2% | – |
| Arena Maths | Arena (LMArena) | 1500† | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 63.0% | – |
| LiveBench 1.0 Data Analysis | LiveBench | 81.6% | 74.2% |
| AA-Omniscience v1.1 | Artificial Analysis | – | -12.2 |
| MMLU-Pro | Vals AI | 88.1% | – |
| LegalBench | Vals AI | 86.5% | – |
| CorpFin | Vals AI | 68.4% | – |
| TaxEval | Vals AI | 75.0% | – |
| CaseLaw v2 | Vals AI | 66.2% | – |
| Excel Modeling Benchmark | Vals AI | 64.5% | – |
| MedCode | Vals AI | 49.1% | – |
| MedScribe | Vals AI | 86.9% | – |
| Public Benefits Bench v1.1 | Vals AI | 60.9% | – |
| DrugDiscoveryBench | Scale AI SEAL | 51.6% | – |
| EBR-bench | Epoch AI Benchmarking Hub | 34.3% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 26.0% | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 23.5% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 53.2% |
| Instruction following | |||
| LiveBench Language | LiveBench | 87.4% | 74.6% |
| LiveBench Instruction Following | LiveBench | 70.7% | 77.1% |
| IFBench | Artificial Analysis | 75.8% | – |
| Arena Instruction Following | Arena (LMArena) | 1475† | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1477† | – |
| EQ-Bench 4 | EQ-Bench | 1295† | – |
| Arena Creative Writing | Arena (LMArena) | 1448† | – |
| Arena Multi-turn | Arena (LMArena) | 1484† | – |
| Arena Longer Queries | Arena (LMArena) | 1487† | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1297† | – |
| MMMU-Pro | Artificial Analysis | 79.9% | 79.8% |
| MMMU-Pro (Vals) | Vals AI | 88.3% | – |
| MortgageTax | Vals AI | 68.8% | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 36.2%† | – |
| Code2Video-AA | Artificial Analysis | 1000 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 84.3% | 79.7% |
| Arena Document | Arena (LMArena) | 1483† | – |
| MLCR | Artificial Analysis | 28.9% | 20.6% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 160.0† | – |
| LiveBench 1.0 | LiveBench | 80.2% | 76.2% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 38.4 | 39.8 |
| AA Progress Index v1 | Artificial Analysis | 320 | 320.2 |
| Vals RSI Index | Vals AI | 16.5% | – |
| Vals Multimodal Index | Vals AI | 68.1% | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 16.3% | – |
Data as of 2026-11-06. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-09b2218cbd85 (https://benchmarks.duellab.org/releases/gb2-20261103-09b2218cbd85/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- GPT-5.5 (xhigh): 1 score revision.
- Qwen3.8-Flash-Next: 1 new result (AA-Briefcase v1.1); 1 score revision.
- Qwen3.8-Flash-Next: BenchLeader Index 58.7 → 54.7.
- GPT-5.5 (xhigh): 1 new result (AA Progress Index v1).
- Qwen3.8-Flash-Next: 2 new results (AA Progress Index v1, HLE-Diamond (AA)); 3 score revisions.
- GPT-5.5 (xhigh): cost per task moved from $2.63/M to $2.10/M (20% down).
- GPT-5.5 (xhigh): 1 new result (AA-Briefcase v1.1).
- GPT-5.5 (xhigh): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
- GPT-5.5 (xhigh): 1 score revision.
- Qwen3.8-Flash-Next: 1 new result (MLCR).
Related comparisons
- GPT-5.5 vs Claude Opus 5.5
- GPT-5.5 vs Gemini 4 Carbon
- GPT-5.5 vs GPT-6 Astra
- Qwen3.8-Flash-Next vs GPT-6 Astra
- Qwen3.8-Flash-Next vs GPT-6.1 Sol
- Qwen3.8-Flash-Next vs Gemini 4 Argon
- Qwen3.8-Flash-Next vs Claude Sonnet 5.5
- GPT-5.5 vs Claude Fable 5.1
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GPT-5.5 vs Qwen3.8-Flash-Next: questions
- Is GPT-5.5 better than Qwen3.8-Flash-Next?
- GPT-5.5 (xhigh) and Qwen3.8-Flash-Next are neck and neck on quality (64.3 vs 54.6, within the ±10.3 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 6 Nov 2026, so check the category scores for your use.
- GPT-5.5 vs Qwen3.8-Flash-Next: which is cheaper, and will that change?
- Qwen3.8-Flash-Next is cheaper today: $0.230 against $11.25 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is GPT-5.5 or Qwen3.8-Flash-Next better for coding?
- Qwen3.8-Flash-Next scores higher in coding (66 vs 61 on the category index, where 50 is average). On Arena WebDev, the top shared coding benchmark, it is 1512 for GPT-5.5 against 1628 for Qwen3.8-Flash-Next.
- Which is faster, GPT-5.5 or Qwen3.8-Flash-Next?
- GPT-5.5 streams ~1.5× faster (79 vs 64 output tokens per second, Artificial Analysis 7-day median).
- Is GPT-5.5 or Qwen3.8-Flash-Next better for agents and tool use?
- Qwen3.8-Flash-Next scores higher in agentic tasks (64 vs 62 on the category index, where 50 is average). On τ³-Banking (AA), the top shared agents & tools benchmark, it is 39.0% for GPT-5.5 against 45.4% for Qwen3.8-Flash-Next.
More questions (4)
- Which is better for long documents?
- GPT-5.5 scores higher on long-context benchmarks (67 vs 65). GPT-5.5 accepts more context: 1,050,000 against 256,000 tokens for Qwen3.8-Flash-Next.
- Which has lower latency?
- GPT-5.5 answers first: 58.0 s against 125.1 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Do independent benchmarks agree on GPT-5.5 vs Qwen3.8-Flash-Next?
- Across the 23 benchmarks both have, GPT-5.5 leads 14, Qwen3.8-Flash-Next leads 9.
- When was Qwen3.8-Flash-Next released?
- Qwen3.8-Flash-Next was released on 26 Aug 2026.