GPT-5.2-Codex vs Schematron V2 Small
Schematron V2 Small costs 60.9x less per output token than GPT-5.2-Codex ($0.23 vs $14.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- 45.4
- Overall
- No data
- Output $/M
- $14.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.23
GPT-5.2-Codex or Schematron V2 Small: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Schematron V2 Small has none, so the two cannot be ranked on coding.
Pick Schematron V2 Small for output tokens 60.9x cheaper ($0.23 vs $14.00 per million).
No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Schematron V2 Small 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-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.2-Codex has a AA Intelligence Index result, 28.5.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Schematron V2 Small
GPT-5.2-Codex $0.0088, Schematron V2 Small $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Schematron V2 Small 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-5.2-Codex $14.00 against Schematron V2 Small $0.23 per million output tokens, 60.9x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Schematron V2 Small 128K.
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 Index11 boards measured
| Benchmark | GPT-5.2-Codex | Schematron V2 Small | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2-Codex | Schematron V2 Small | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-5.2-Codex | Schematron V2 Small | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.2-Codex | Schematron V2 Small | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 20 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.2-Codex | Schematron V2 Small | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | Not measured | Only GPT-5.2-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
Schematron V2 Small costs less
Repository review
50K input + 3K output tokens
Schematron V2 Small costs less
Agent loop
200K input + 10K output tokens
Schematron V2 Small 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-5.2-Codex | Inference.net Schematron V2 Small |
|---|---|---|
| Maker | OpenAI | Inference.net |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | September 12, 2026 |
| Context window | 400K | 128K |
| Input $/Mtok | $1.75 | $0.05 |
| Output $/Mtok | $14.00 | $0.23 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Schematron V2 Small for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Schematron V2 Small has none, so the two cannot be ranked on coding. Pick Schematron V2 Small for output tokens 60.9x cheaper ($0.23 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Schematron V2 Small?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Schematron V2 Small costs $0.05 and $0.23. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Schematron V2 Small?
GPT-5.2-Codex, with 400K tokens against 128K.
Has any benchmark tested both GPT-5.2-Codex and Schematron V2 Small?
No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Schematron V2 Small has none yet. Compare them on price and context instead. Data as of October 22, 2026.