GPT-5 Nano vs Hy3
GPT-5 Nano costs 1.3x less per output token than Hy3 ($0.40 vs $0.528 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
GPT-5 Nano or Hy3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.528 per million) and a larger context window (400K against 262K tokens).
- Shared results
- 36
- GPT-5 Nano only
- 51
- Hy3 only
- 8
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5 Nano has a AA Intelligence Index result, 13.0.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 Nano has a GPQA Diamond result, 63.4%.
Directional only
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Nano has a MMLU Pro result, 76.1%.
Directional only
Web design
Design Arena: websites
Hy3
Hy3 leads, 1230 against 1101.
Same board, both measured
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Hy3 $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Nano has the number, $0.109.
Directional only
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Hy3 not benchmarked.
- Widest category
- Math, 26.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5 Nano 45.0% against Hy3 79.0%, 34.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Hy3 $0.528 per million output tokens, 1.3x apart.
- Context window
- GPT-5 Nano takes 400K tokens, Hy3 262K.
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 Nano | Bars | Hy3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 36 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| LMArena WebDev | Not measured | 1507 | Only Hy3 measured |
| LiveCodeBench (Vals AI) | 70.2% | Not measured | Only GPT-5 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only GPT-5 Nano measured |
| SciCode | Not measured | 48.6% | Only Hy3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| ALE-Bench | Not measured | 838.6 | Only Hy3 measured |
| LMArena Text: coding | 1351 | 1466 | Hy3 |
| LMArena Text: software and IT | 1345 | 1463 | Hy3 |
| LMArena WebDev: React | Not measured | 1511 | Only Hy3 measured |
| LMArena WebDev: HTML | Not measured | 1487 | Only Hy3 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | Not measured | Only GPT-5 Nano measured |
| Epoch Capabilities Index | 139.2 | Not measured | Only GPT-5 Nano measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | Not measured | Only GPT-5 Nano measured |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | Not measured | Only GPT-5 Nano measured |
| MMMU Pro (Vals AI) | 70.9% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 67.6% | 89.7% | Hy3 |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 33.5% | Hy3 |
| GPQA Diamond (Epoch AI run) | 69.4% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI | 20.7% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI-2 | 2.6% | Not measured | Only GPT-5 Nano measured |
| Chess puzzles | 22.0% | Not measured | Only GPT-5 Nano measured |
| Mystery game puzzles | 9.0% | Not measured | Only GPT-5 Nano measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | 1447 | Hy3 |
| LMArena Text: expert | 1321 | 1461 | Hy3 |
| LMArena Text: hard prompts in English | 1338 | 1455 | Hy3 |
| LMArena Vision: diagrams | 1141 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: homework | 1157 | Not measured | Only GPT-5 Nano measured |
| Kagi LLM Benchmark | 62.2% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| AIME | 81.2% | Not measured | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | 93.8% | Not measured | Only GPT-5 Nano measured |
| MGSM (Vals AI) | 89.3% | Not measured | Only GPT-5 Nano measured |
| AA Math Index | 83.7 | Not measured | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | 83.7% | Not measured | Only GPT-5 Nano measured |
| OTIS Mock AIME | 81.1% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tiers 1 to 3 | 20.0% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only GPT-5 Nano measured |
| ProofBench | 12.0% | Not measured | Only GPT-5 Nano measured |
| MATH level 5 | 95.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: math | 1317 | 1473 | Hy3 |
| LMArena Text: mathematical work | 1326 | 1482 | Hy3 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | Not measured | Only GPT-5 Nano measured |
| LegalBench (Vals AI) | 50.1% | Not measured | Only GPT-5 Nano measured |
| CaseLaw (Vals AI) | 52.6% | Not measured | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | 67.4% | Not measured | Only GPT-5 Nano measured |
| MortgageTax (Vals AI) | 53.6% | Not measured | Only GPT-5 Nano measured |
| MedQA (Vals AI) | 93.3% | Not measured | Only GPT-5 Nano measured |
| MedCode (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | 78.0% | Not measured | Only GPT-5 Nano measured |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1457 | Hy3 |
| LMArena Text: medicine and healthcare | 1335 | 1452 | Hy3 |
| LMArena Text: legal and government | 1298 | 1422 | Hy3 |
| LMArena Text: business and finance | 1311 | 1424 | Hy3 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 1230 | Hy3 |
| Design Arena: UI components | 1064 | 1204 | Hy3 |
| Design Arena: game dev | 1044 | 1208 | Hy3 |
| Design Arena: data viz | 1056 | 1164 | Hy3 |
| Design Arena: 3D | 965 | 1232 | Hy3 |
| Design Arena: all code categories | 1084 | 1227 | Hy3 |
| Design Arena: SVG | Not measured | 1200 | Only Hy3 measured |
| Design Arena: ASCII art | Not measured | 1152 | Only Hy3 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | GPT-5 Nano | Hy3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 72.9% | Not measured | Only GPT-5 Nano measured |
| IFBench | 67.6% | Not measured | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 45.0% | 79.0% | Hy3 |
| DTBench | 62.7% | Not measured | Only GPT-5 Nano measured |
| LMCA | 7.9% | Not measured | Only GPT-5 Nano measured |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | 1438 | Hy3 |
| LMArena Text: instruction following | 1306 | 1426 | Hy3 |
| LMArena Text: creative writing | 1249 | 1403 | Hy3 |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | 1448 | Hy3 |
| LMArena Text: non-English | 1313 | 1426 | Hy3 |
| LMArena Text: Chinese | 1356 | 1485 | Hy3 |
| LMArena Text: German | 1327 | 1449 | Hy3 |
| LMArena Text: Japanese | 1227 | 1397 | Hy3 |
| LMArena Text: Korean | 1269 | 1396 | Hy3 |
| LMArena Text: French | Not measured | 1456 | Only Hy3 measured |
| LMArena Text: Spanish | 1360 | 1457 | Hy3 |
| LMArena Text: Russian | 1296 | 1432 | Hy3 |
| LMArena Text: Polish | 1314 | 1447 | Hy3 |
| LMArena Text: multi-turn | 1311 | 1436 | Hy3 |
| LMArena Text: longer queries | 1312 | 1442 | Hy3 |
| LMArena Text: writing and language | 1283 | 1414 | Hy3 |
| LMArena Text: entertainment and media | 1268 | 1400 | Hy3 |
| LMArena Vision: English | 1170 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | 1176 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: reading text | 1152 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: humor | 1110 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | Hy3 | 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
GPT-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 Nano 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 Nano | Tencent Hy3 |
|---|---|---|
| Maker | OpenAI | Tencent |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | July 6, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.05 | $0.132 |
| Output $/Mtok | $0.40 | $0.528 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | $0.109 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Hy3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.528 per million) and a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5 Nano or Hy3?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Hy3 costs $0.132 and $0.528. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, GPT-5 Nano or Hy3?
GPT-5 Nano, with 400K tokens against 262K.
How many benchmarks is the GPT-5 Nano vs Hy3 comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 independent benchmarks and Hy3's on 1. Where only one model has a result, the page says so instead of naming a winner.