Nova Micro 1.0 vs GPT-3.5 Turbo
Nova Micro 1.0 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 5.9 to 5.5, and Nova Micro 1.0 costs 10.7x less per output token ($0.14 vs $1.50 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Nova Micro 1.0 or GPT-3.5 Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Micro 1.0 for output tokens 10.7x cheaper ($0.14 vs $1.50 per million) and a larger context window (128K against 16K tokens).
- Shared results
- 4
- Nova Micro 1.0 only
- 35
- GPT-3.5 Turbo only
- 9
- 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
Nova Micro 1.0
Nova Micro 1.0 leads, 5.9 against 5.5.
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
Nova Micro 1.0
Nova Micro 1.0 takes 128K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nova Micro 1.0
Nova Micro 1.0 $0.0001, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- Nova Micro 1.0 not benchmarked, GPT-3.5 Turbo not benchmarked.
- Widest category
- Reasoning, 6.1 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Nova Micro 1.0 53.1% against GPT-3.5 Turbo 46.2%, 6.9 points apart on a 0 to 100 scale.
- Output price
- Nova Micro 1.0 $0.14 against GPT-3.5 Turbo $1.50 per million output tokens, 10.7x apart.
- Context window
- Nova Micro 1.0 takes 128K tokens, GPT-3.5 Turbo 16K.
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 | Nova Micro 1.0 | Bars | GPT-3.5 Turbo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Knowledge | 2 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 10.7 | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | 14.0% | Not measured | Only Nova Micro 1.0 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: coding | 1218 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: software and IT | 1215 | Not measured | Only Nova Micro 1.0 measured |
| SciCode | Not measured | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.9 | 5.5 | Nova Micro 1.0 |
| 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 | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | Not measured | Only Nova Micro 1.0 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 35.8% | 29.7% | Nova Micro 1.0 |
| Humanity's Last Exam (Artificial Analysis run) | 4.6% | Not measured | Only Nova Micro 1.0 measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | 1191 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: expert | 1183 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: hard prompts in English | 1203 | Not measured | Only Nova Micro 1.0 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only Nova Micro 1.0 measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only Nova Micro 1.0 measured |
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| LMArena Text: math | 1206 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: mathematical work | 1212 | Not measured | Only Nova Micro 1.0 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 64.4% | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | Not measured | 58.5% | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 53.1% | 46.2% | Nova Micro 1.0 |
| MMLU | 70.8% | 68.5% | Nova Micro 1.0 |
| LMArena Text: science | 1210 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: medicine and healthcare | 1207 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: legal and government | 1221 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: business and finance | 1182 | Not measured | Only Nova Micro 1.0 measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| IFBench | 29.4% | Not measured | Only Nova Micro 1.0 measured |
| Long context reasoning (AA-LCR) | 13.0% | Not measured | Only Nova Micro 1.0 measured |
| DTBench | Not measured | 48.5% | Only GPT-3.5 Turbo measured |
| LMCA | Not measured | 9.7% | Only GPT-3.5 Turbo measured |
| LMArena Text | 1208 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: instruction following | 1174 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: creative writing | 1172 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: English | 1224 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: non-English | 1186 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Chinese | 1209 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: German | 1192 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Japanese | 1154 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Korean | 1150 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: French | 1238 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Spanish | 1225 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: Russian | 1185 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: multi-turn | 1178 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: longer queries | 1205 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: writing and language | 1192 | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: entertainment and media | 1163 | Not measured | Only Nova Micro 1.0 measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Micro 1.0 | GPT-3.5 Turbo | 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
Nova Micro 1.0 costs less
Repository review
50K input + 3K output tokens
Nova Micro 1.0 costs less
Agent loop
200K input + 10K output tokens
Nova Micro 1.0 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 | Amazon Nova Micro 1.0 | OpenAI GPT-3.5 Turbo |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 5, 2024 | May 28, 2023 |
| Context window | 128K | 16K |
| Input $/Mtok | $0.035 | $0.50 |
| Output $/Mtok | $0.14 | $1.50 |
| 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 Nova Micro 1.0 better than GPT-3.5 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Micro 1.0 for output tokens 10.7x cheaper ($0.14 vs $1.50 per million) and a larger context window (128K against 16K tokens).
Which is cheaper, Nova Micro 1.0 or GPT-3.5 Turbo?
Nova Micro 1.0 costs $0.035 per million input tokens and $0.14 per million output tokens; GPT-3.5 Turbo costs $0.50 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Micro 1.0 or GPT-3.5 Turbo?
Nova Micro 1.0, with 128K tokens against 16K.
How many benchmarks is the Nova Micro 1.0 vs GPT-3.5 Turbo comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Nova Micro 1.0's coding index rests on 0 independent benchmarks and GPT-3.5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.