GPT-5.5 vs Qwen3.8 Omni Flash
Qwen3.8 Omni Flash costs 63.8x less per output token than GPT-5.5 ($0.47 vs $30.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
GPT-5.5OpenAI
- Coding
- 71.3
- Overall
- 69.2
- Output $/M
- $30.00
Qwen3.8 Omni FlashQwen
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
vs $30.00 vs $0.47 per 1M output tokens. Qwen3.8 Omni Flash is 63.8x cheaper.
GPT-5.5 or Qwen3.8 Omni Flash: which to pick
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Qwen3.8 Omni Flash has none, so the two cannot be ranked on coding.
Pick Qwen3.8 Omni Flash for output tokens 63.8x cheaper ($0.47 vs $30.00 per million).
Model AGPT-5.5OpenAI71.3/1005 independent benchmarks$1.65 per solved taskModel BQwen3.8 Omni FlashQwenNot benchmarkedNo coding benchmarkNo cost per solved task
No benchmark has tested both yet. GPT-5.5 has results on LiveBench coding, LMArena WebDev and AA Coding Index; Qwen3.8 Omni Flash has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.5 has a AA Intelligence Index result, 38.4.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.5 has a Design Arena: websites result, 1256.
Directional only
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Omni Flash
GPT-5.5 $0.020, Qwen3.8 Omni Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only
At a glance
- Coding index
- GPT-5.5 71.3 from 5 benchmarks, Qwen3.8 Omni Flash not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.5 $30.00 against Qwen3.8 Omni Flash $0.47 per million output tokens, 63.8x apart.
- Context window
- GPT-5.5 takes 1.05M tokens, Qwen3.8 Omni Flash 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index26 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | Not measured | Only GPT-5.5 measured |
| AA Coding Index | 74.9 | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | 85.3% | Not measured | Only GPT-5.5 measured |
| SWE-bench Verified (Vals AI) | 82.6% | Not measured | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | 69.8% | Not measured | Only GPT-5.5 measured |
| Code migration (Vals AI) | 45.2% | Not measured | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | Not measured | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | 62.5% | Not measured | Only GPT-5.5 measured |
| SciCode | 56.1% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | Not measured | Only GPT-5.5 measured |
| GSO-Bench | 40.2% | Not measured | Only GPT-5.5 measured |
| WeirdML | 84.9% | Not measured | Only GPT-5.5 measured |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | Not measured | Only GPT-5.5 measured |
| LMArena Text: coding | 1494 | Not measured | Only GPT-5.5 measured |
| LMArena Text: software and IT | 1493 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: React | 1506 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: HTML | 1543 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: from an image | 1532 | Not measured | Only GPT-5.5 measured |
| SWE Atlas: Codebase QnA | 45.4% | Not measured | Only GPT-5.5 measured |
| SWE Atlas: Refactoring | 44.8% | Not measured | Only GPT-5.5 measured |
| SWE Atlas: Test Writing | 42.6% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | Not measured | Only GPT-5.5 measured |
| Epoch Capabilities Index | 159.7 | Not measured | Only GPT-5.5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index26 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building web apps | 1117 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | 1089 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | 1142 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | 1163 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1173 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building games | 1178 | Not measured | Only GPT-5.5 measured |
| Design Arena: HTML slides | 1006 | Not measured | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | 1083 | Not measured | Only GPT-5.5 measured |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | Not measured | Only GPT-5.5 measured |
| APEX-Agents | 55.1% | Not measured | Only GPT-5.5 measured |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | Not measured | Only GPT-5.5 measured |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| Vending-Bench 2 | $7,524 | Not measured | Only GPT-5.5 measured |
| MCP Atlas | 75.3% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | Not measured | Only GPT-5.5 measured |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 93.5% | Not measured | Only GPT-5.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | Not measured | Only GPT-5.5 measured |
| GPQA Diamond (Epoch AI run) | 90.7% | Not measured | Only GPT-5.5 measured |
| ARC-AGI | 95.0% | Not measured | Only GPT-5.5 measured |
| ARC-AGI-2 | 85.0% | Not measured | Only GPT-5.5 measured |
| SimpleBench | 69.0% | Not measured | Only GPT-5.5 measured |
| Chess puzzles | 63.0% | Not measured | Only GPT-5.5 measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-5.5 measured |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1489 | Not measured | Only GPT-5.5 measured |
| LMArena Text: expert | 1507 | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts in English | 1483 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: diagrams | 1325 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: homework | 1332 | Not measured | Only GPT-5.5 measured |
| Kagi LLM Benchmark | 88.8% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 84.4% | Not measured | Only GPT-5.5 measured |
| FrontierMath, tiers 1 to 3 | 85.3% | Not measured | Only GPT-5.5 measured |
| FrontierMath, tier 4 | 72.5% | Not measured | Only GPT-5.5 measured |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: math | 1487 | Not measured | Only GPT-5.5 measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.5 measured |
| MathArena | 61.3% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | Not measured | Only GPT-5.5 measured |
| LegalBench (Vals AI) | 86.5% | Not measured | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | 66.2% | Not measured | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 68.4% | Not measured | Only GPT-5.5 measured |
| TaxEval (Vals AI) | 75.0% | Not measured | Only GPT-5.5 measured |
| MortgageTax (Vals AI) | 68.8% | Not measured | Only GPT-5.5 measured |
| MedCode (Vals AI) | 49.1% | Not measured | Only GPT-5.5 measured |
| SimpleQA Verified | 63.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: science | 1485 | Not measured | Only GPT-5.5 measured |
| LMArena Text: medicine and healthcare | 1465 | Not measured | Only GPT-5.5 measured |
| LMArena Text: legal and government | 1491 | Not measured | Only GPT-5.5 measured |
| LMArena Text: business and finance | 1478 | Not measured | Only GPT-5.5 measured |
| FACTS Benchmark Suite | 66.7% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | Not measured | Only GPT-5.5 measured |
| Design Arena: UI components | 1257 | Not measured | Only GPT-5.5 measured |
| Design Arena: game dev | 1293 | Not measured | Only GPT-5.5 measured |
| Design Arena: data viz | 1264 | Not measured | Only GPT-5.5 measured |
| Design Arena: 3D | 1197 | Not measured | Only GPT-5.5 measured |
| Design Arena: all code categories | 1259 | Not measured | Only GPT-5.5 measured |
| Design Arena: SVG | 1233 | Not measured | Only GPT-5.5 measured |
| Design Arena: ASCII art | 1255 | Not measured | Only GPT-5.5 measured |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index39 boards measured
| Benchmark | GPT-5.5 | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | Not measured | Only GPT-5.5 measured |
| MedScribe (Vals AI) | 86.9% | Not measured | Only GPT-5.5 measured |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | Not measured | Only GPT-5.5 measured |
| Long context reasoning (AA-LCR) | 84.3% | Not measured | Only GPT-5.5 measured |
| DTBench | 96.0% | Not measured | Only GPT-5.5 measured |
| LMCA | 54.3% | Not measured | Only GPT-5.5 measured |
| Surface Evolver Bench | 88.1% | Not measured | Only GPT-5.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | Not measured | Only GPT-5.5 measured |
| LMArena Text: instruction following | 1478 | Not measured | Only GPT-5.5 measured |
| LMArena Text: creative writing | 1455 | Not measured | Only GPT-5.5 measured |
| LMArena Vision | 1297 | Not measured | Only GPT-5.5 measured |
| LMArena Text: English | 1470 | Not measured | Only GPT-5.5 measured |
| LMArena Text: non-English | 1467 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Chinese | 1534 | Not measured | Only GPT-5.5 measured |
| LMArena Text: German | 1480 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Japanese | 1499 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GPT-5.5 measured |
| LMArena Text: French | 1485 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Spanish | 1468 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Russian | 1473 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Polish | 1465 | Not measured | Only GPT-5.5 measured |
| LMArena Text: multi-turn | 1476 | Not measured | Only GPT-5.5 measured |
| LMArena Text: longer queries | 1484 | Not measured | Only GPT-5.5 measured |
| LMArena Text: writing and language | 1466 | Not measured | Only GPT-5.5 measured |
| LMArena Text: entertainment and media | 1449 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: English | 1292 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: Chinese | 1360 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: reading text | 1306 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: humor | 1293 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | 1231 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-5.5 measured |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| EQ-Bench 4 | 1315 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 9 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-5.5$0.020Fits in one request
Qwen3.8 Omni Flash$0.0004Fits in one request
Qwen3.8 Omni Flash costs less
Repository review
50K input + 3K output tokens
GPT-5.5$0.340Fits in one request
Qwen3.8 Omni Flash$0.0089Fits in one request
Qwen3.8 Omni Flash costs less
Agent loop
200K input + 10K output tokens
GPT-5.5$1.30Fits in one request
Qwen3.8 Omni Flash$0.035Fits in one request
Qwen3.8 Omni Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.5 | Qwen3.8 Omni Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | September 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $5.00 | $0.15 |
| Output $/Mtok | $30.00 | $0.47 |
| Coding index | 71.3 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $1.65 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than Qwen3.8 Omni Flash for coding?
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Qwen3.8 Omni Flash has none, so the two cannot be ranked on coding. Pick Qwen3.8 Omni Flash for output tokens 63.8x cheaper ($0.47 vs $30.00 per million).
Which is cheaper, GPT-5.5 or Qwen3.8 Omni Flash?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Qwen3.8 Omni Flash costs $0.15 and $0.47. Cost per solved task is known for GPT-5.5 only.
Which has the bigger context window, GPT-5.5 or Qwen3.8 Omni Flash?
GPT-5.5, with 1.05M tokens against 1M.
Has any benchmark tested both GPT-5.5 and Qwen3.8 Omni Flash?
No benchmark has tested both yet. GPT-5.5 has results on LiveBench coding, LMArena WebDev and AA Coding Index; Qwen3.8 Omni Flash has none yet. Compare them on price and context instead. Data as of October 22, 2026.