GPT-5.3-Codex vs GPT-5.6 Sol Pro
GPT-5.3-Codex costs 1.4x less per output token than GPT-5.6 Sol Pro ($14.00 vs $20.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
GPT-5.3-Codex or GPT-5.6 Sol Pro: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 400K tokens) and work measured by WeirdML, where it leads.
- Shared results
- 1
- GPT-5.3-Codex only
- 33
- GPT-5.6 Sol Pro only
- 6
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, 1163.
Directional only
Long documents
Listed context window
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.3-Codex
GPT-5.3-Codex $0.0088, GPT-5.6 Sol Pro $0.014 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, GPT-5.6 Sol Pro not benchmarked.
- Widest category
- Coding, 10.1 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: GPT-5.3-Codex 79.3% against GPT-5.6 Sol Pro 89.4%, 10.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against GPT-5.6 Sol Pro $20.00 per million output tokens, 1.4x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, GPT-5.6 Sol Pro 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.3-Codex | Bars | GPT-5.6 Sol Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | 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% | 89.4% | GPT-5.6 Sol Pro |
| 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, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | 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 index9 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | 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 | 1054 | 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 |
| APEX-Agents | Not measured | 51.4% | Only GPT-5.6 Sol Pro 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 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | 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 |
| SimpleBench | Not measured | 71.7% | Only GPT-5.6 Sol Pro measured |
| Chess puzzles | Not measured | 68.0% | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | Leader |
|---|---|---|---|
| FrontierMath, tier 4 | Not measured | 80.5% | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: UI components | 1137 | 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 | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | 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 index4 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | 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 |
| DTBench | Not measured | 96.0% | Only GPT-5.6 Sol Pro measured |
| LMCA | Not measured | 59.2% | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Sol Pro | Leader |
|---|---|---|---|
| Neither model has a result on 5 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-5.3-Codex costs less
Repository review
50K input + 3K output tokens
GPT-5.3-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.3-Codex 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 | OpenAI GPT-5.6 Sol Pro |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | July 9, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $4.00 |
| Output $/Mtok | $14.00 | $20.00 |
| 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 GPT-5.6 Sol Pro for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 400K tokens) and work measured by WeirdML, where it leads.
Which is cheaper, GPT-5.3-Codex or GPT-5.6 Sol Pro?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; GPT-5.6 Sol Pro costs $4.00 and $20.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or GPT-5.6 Sol Pro?
GPT-5.6 Sol Pro, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs GPT-5.6 Sol Pro comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and GPT-5.6 Sol Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.