GPT-5.3-Codex vs Solar Pro 4
GPT-5.3-Codex leads Solar Pro 4 on the Artificial Analysis Intelligence Index, 32.5 to 28.2, and Solar Pro 4 costs 23.3x less per output token ($0.60 vs $14.00 per million). Updated October 22, 2026; 15 benchmarks have a result for both.
GPT-5.3-Codex or Solar Pro 4: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Solar Pro 4 has none, so the two cannot be ranked on coding.
Pick Solar Pro 4 for output tokens 23.3x cheaper ($0.60 vs $14.00 per million) and a larger context window (524K against 400K tokens).
- Shared results
- 15
- GPT-5.3-Codex only
- 19
- Solar Pro 4 only
- 30
- Like-for-like categories
- 6 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
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 28.2.
Same board, both measured
Web design
Design Arena: websites
Solar Pro 4
Solar Pro 4 leads, 1176 against 1162.
Same board, both measured
Long documents
Listed context window
Solar Pro 4
Solar Pro 4 takes 524K tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Solar Pro 4
GPT-5.3-Codex $0.0088, Solar Pro 4 $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, Solar Pro 4 not benchmarked.
- Widest category
- Coding, 17.0 points apart on average over 4 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5.3-Codex 1655.2 against Solar Pro 4 179.3, 49.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Solar Pro 4 $0.60 per million output tokens, 23.3x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Solar Pro 4 524K.
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 | Solar Pro 4 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| Web design | 0 | 6 | ||
| General | 1 | 0 | ||
| All shared boards | 8 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | 1371 | GPT-5.3-Codex |
| AA Coding Index | Not measured | 52.7 | Only Solar Pro 4 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 |
| SciCode | Not measured | 48.0% | Only Solar Pro 4 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 | 179.3 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1438 | Only Solar Pro 4 measured |
| LMArena Text: software and IT | Not measured | 1431 | Only Solar Pro 4 measured |
| LMArena WebDev: React | 1395 | 1363 | GPT-5.3-Codex |
| LMArena WebDev: HTML | 1422 | 1371 | GPT-5.3-Codex |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 28.2 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | 1105 | Solar Pro 4 |
| 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 | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | 89.1% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 29.8% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1400 | Only Solar Pro 4 measured |
| LMArena Text: expert | Not measured | 1428 | Only Solar Pro 4 measured |
| LMArena Text: hard prompts in English | Not measured | 1427 | Only Solar Pro 4 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1421 | Only Solar Pro 4 measured |
| LMArena Text: mathematical work | Not measured | 1427 | Only Solar Pro 4 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1404 | Only Solar Pro 4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1374 | Only Solar Pro 4 measured |
| LMArena Text: legal and government | Not measured | 1383 | Only Solar Pro 4 measured |
| LMArena Text: business and finance | Not measured | 1381 | Only Solar Pro 4 measured |
| Neither model has a result on the other 14 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1176 | Solar Pro 4 |
| Design Arena: UI components | 1134 | 1147 | Solar Pro 4 |
| Design Arena: game dev | 1161 | 1169 | Solar Pro 4 |
| Design Arena: data viz | 1164 | 1177 | Solar Pro 4 |
| Design Arena: 3D | 1007 | 1178 | Solar Pro 4 |
| Design Arena: all code categories | 1144 | 1182 | Solar Pro 4 |
| 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | 70.7% | GPT-5.3-Codex |
| LMArena Text | Not measured | 1388 | Only Solar Pro 4 measured |
| LMArena Text: instruction following | Not measured | 1375 | Only Solar Pro 4 measured |
| LMArena Text: creative writing | Not measured | 1317 | Only Solar Pro 4 measured |
| LMArena Text: English | Not measured | 1414 | Only Solar Pro 4 measured |
| LMArena Text: non-English | Not measured | 1363 | Only Solar Pro 4 measured |
| LMArena Text: Chinese | Not measured | 1422 | Only Solar Pro 4 measured |
| LMArena Text: German | Not measured | 1358 | Only Solar Pro 4 measured |
| LMArena Text: Japanese | Not measured | 1320 | Only Solar Pro 4 measured |
| LMArena Text: Korean | Not measured | 1383 | Only Solar Pro 4 measured |
| LMArena Text: French | Not measured | 1400 | Only Solar Pro 4 measured |
| LMArena Text: Spanish | Not measured | 1409 | Only Solar Pro 4 measured |
| LMArena Text: Russian | Not measured | 1357 | Only Solar Pro 4 measured |
| LMArena Text: Polish | Not measured | 1304 | Only Solar Pro 4 measured |
| LMArena Text: multi-turn | Not measured | 1385 | Only Solar Pro 4 measured |
| LMArena Text: longer queries | Not measured | 1382 | Only Solar Pro 4 measured |
| LMArena Text: writing and language | Not measured | 1344 | Only Solar Pro 4 measured |
| LMArena Text: entertainment and media | Not measured | 1332 | Only Solar Pro 4 measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Solar Pro 4 | 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
Solar Pro 4 costs less
Repository review
50K input + 3K output tokens
Solar Pro 4 costs less
Agent loop
200K input + 10K output tokens
Solar Pro 4 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 | Upstage Solar Pro 4 |
|---|---|---|
| Maker | OpenAI | Upstage |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | August 10, 2026 |
| Context window | 400K | 524K |
| Input $/Mtok | $1.75 | $0.15 |
| Output $/Mtok | $14.00 | $0.60 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Solar Pro 4 for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Solar Pro 4 has none, so the two cannot be ranked on coding. Pick Solar Pro 4 for output tokens 23.3x cheaper ($0.60 vs $14.00 per million) and a larger context window (524K against 400K tokens).
Which is cheaper, GPT-5.3-Codex or Solar Pro 4?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Solar Pro 4 costs $0.15 and $0.60. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Solar Pro 4?
Solar Pro 4, with 524K tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs Solar Pro 4 comparison based on?
15 benchmarks have a result for both models as of October 22, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Solar Pro 4's on 1. Where only one model has a result, the page says so instead of naming a winner.