GPT-5.3-Codex vs Qwen3.8 Omni Flash
Qwen3.8 Omni Flash costs 29.8x less per output token than GPT-5.3-Codex ($0.47 vs $14.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- 60.0
- Overall
- No data
- Output $/M
- $14.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-5.3-Codex or Qwen3.8 Omni Flash: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 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 29.8x cheaper ($0.47 vs $14.00 per million) and a larger context window (1M against 400K tokens).
No benchmark has tested both yet. GPT-5.3-Codex has results on LMArena WebDev, LiveCodeBench (Vals AI) and SWE-bench Verified (Vals AI); 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-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1162.
Directional only
Long documents
Listed context window
Qwen3.8 Omni Flash
Qwen3.8 Omni Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Omni Flash
GPT-5.3-Codex $0.0088, Qwen3.8 Omni Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 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-5.3-Codex $14.00 against Qwen3.8 Omni Flash $0.47 per million output tokens, 29.8x apart.
- Context window
- GPT-5.3-Codex takes 400K 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 Index12 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | Not measured | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | Not measured | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: UI components | 1134 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: game dev | 1161 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: data viz | 1164 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: 3D | 1007 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1144 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.3-Codex | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 44 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
Qwen3.8 Omni Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Omni Flash costs less
Agent loop
200K input + 10K output tokens
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-5.3-Codex | Qwen3.8 Omni Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | September 21, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $0.15 |
| Output $/Mtok | $14.00 | $0.47 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Qwen3.8 Omni Flash for coding?
Only GPT-5.3-Codex has a coding score, 60.0 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 29.8x cheaper ($0.47 vs $14.00 per million) and a larger context window (1M against 400K tokens).
Which is cheaper, GPT-5.3-Codex or Qwen3.8 Omni Flash?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.8 Omni Flash costs $0.15 and $0.47. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Qwen3.8 Omni Flash?
Qwen3.8 Omni Flash, with 1M tokens against 400K.
Has any benchmark tested both GPT-5.3-Codex and Qwen3.8 Omni Flash?
No benchmark has tested both yet. GPT-5.3-Codex has results on LMArena WebDev, LiveCodeBench (Vals AI) and SWE-bench Verified (Vals AI); Qwen3.8 Omni Flash has none yet. Compare them on price and context instead. Data as of October 22, 2026.