GPT-3.5 Turbo vs Granite 4.0 Micro
GPT-3.5 Turbo leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 5.5 to 5.1, and Granite 4.0 Micro costs 13.4x less per output token ($0.112 vs $1.50 per million). Updated October 23, 2026; 3 benchmarks have a result for both.
GPT-3.5 Turbo or Granite 4.0 Micro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for work measured by GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 3
- GPT-3.5 Turbo only
- 10
- Granite 4.0 Micro only
- 10
- Like-for-like categories
- 3 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-3.5 Turbo
GPT-3.5 Turbo leads, 5.5 against 5.1.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Granite 4.0 Micro
Granite 4.0 Micro takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
GPT-3.5 Turbo $0.0013, Granite 4.0 Micro $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Granite 4.0 Micro not benchmarked.
- Widest category
- Reasoning, 3.9 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against Granite 4.0 Micro 33.6%, 3.9 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against Granite 4.0 Micro $0.112 per million output tokens, 13.4x apart.
- Context window
- GPT-3.5 Turbo takes 16K 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-3.5 Turbo | Bars | Granite 4.0 Micro | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 2 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 18.0% | Only Granite 4.0 Micro measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Granite 4.0 Micro measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 5.1 | GPT-3.5 Turbo |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 12.6% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 33.6% | Granite 4.0 Micro |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.0% | Only Granite 4.0 Micro measured |
| GPQA Diamond (Epoch AI run) | Not measured | 28.3% | Only Granite 4.0 Micro measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | 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 |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | 44.7% | GPT-3.5 Turbo |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Granite 4.0 Micro | Leader |
|---|---|---|---|
| IFBench | Not measured | 24.8% | Only Granite 4.0 Micro measured |
| Long context reasoning (AA-LCR) | Not measured | 7.0% | Only Granite 4.0 Micro measured |
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | 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-3.5 Turbo | IBM Granite 4.0 Micro |
|---|---|---|
| Maker | OpenAI | IBM Granite |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | October 20, 2025 |
| Context window | 16K | 131K |
| Input $/Mtok | $0.50 | $0.017 |
| Output $/Mtok | $1.50 | $0.112 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than Granite 4.0 Micro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for work measured by GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-3.5 Turbo or Granite 4.0 Micro?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Granite 4.0 Micro costs $0.017 and $0.112. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or Granite 4.0 Micro?
Granite 4.0 Micro, with 131K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs Granite 4.0 Micro comparison based on?
3 benchmarks have a result for both models as of October 23, 2026. GPT-3.5 Turbo's coding index rests on 0 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.