GPT-5.2-Codex vs Granite 4.0 Micro
GPT-5.2-Codex leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 28.5 to 5.1, and Granite 4.0 Micro costs 125.0x less per output token ($0.112 vs $14.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GPT-5.2-Codex or Granite 4.0 Micro: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Granite 4.0 Micro has none, so the two cannot be ranked on coding.
Pick Granite 4.0 Micro for output tokens 125.0x cheaper ($0.112 vs $14.00 per million).
- Shared results
- 7
- GPT-5.2-Codex only
- 15
- Granite 4.0 Micro only
- 6
- Like-for-like categories
- 5 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-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 5.1.
Same board, both measured
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
GPT-5.2-Codex $0.0088, Granite 4.0 Micro $0.0001 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, Granite 4.0 Micro not benchmarked.
- Widest category
- Agentic work, 79.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5.2-Codex 92.1% against Granite 4.0 Micro 12.6%, 79.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Granite 4.0 Micro $0.112 per million output tokens, 125.0x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Granite 4.0 Micro 131K.
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-5.2-Codex | Bars | Granite 4.0 Micro | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 18.0% | Only Granite 4.0 Micro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 1.5% | GPT-5.2-Codex |
| 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 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 5.1 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | 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 | 1089 | 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% | 12.6% | GPT-5.2-Codex |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 33.6% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 5.0% | GPT-5.2-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 28.3% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 6.0 | Only Granite 4.0 Micro measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 6.0% | Only Granite 4.0 Micro measured |
| OTIS Mock AIME | Not measured | 2.8% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 44.7% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 19 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | Leader |
|---|---|---|---|
| IFBench | 77.6% | 24.8% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 82.3% | 7.0% | GPT-5.2-Codex |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Granite 4.0 Micro | 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
Granite 4.0 Micro costs less
Repository review
50K input + 3K output tokens
Granite 4.0 Micro costs less
Agent loop
200K input + 10K output tokens
Granite 4.0 Micro 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 | IBM Granite 4.0 Micro |
|---|---|---|
| Maker | OpenAI | IBM Granite |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | October 20, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.75 | $0.017 |
| Output $/Mtok | $14.00 | $0.112 |
| 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 Granite 4.0 Micro for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Granite 4.0 Micro has none, so the two cannot be ranked on coding. Pick Granite 4.0 Micro for output tokens 125.0x cheaper ($0.112 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Granite 4.0 Micro?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Granite 4.0 Micro costs $0.017 and $0.112. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Granite 4.0 Micro?
GPT-5.2-Codex, with 400K tokens against 131K.
How many benchmarks is the GPT-5.2-Codex vs Granite 4.0 Micro comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Granite 4.0 Micro's on 0. Where only one model has a result, the page says so instead of naming a winner.