GPT-5.1-Codex-Mini vs Hy4 preview
GPT-5.1-Codex-Mini costs 1.3x less per output token than Hy4 preview ($2.00 vs $2.501 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Hy4 preview: which to pick
Only Hy4 preview has a coding score, 44.3 on the Vibe Coding Index; GPT-5.1-Codex-Mini has none, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for output tokens 1.3x cheaper ($2.00 vs $2.501 per million).
- Shared results
- 2
- GPT-5.1-Codex-Mini only
- 20
- Hy4 preview only
- 22
- Like-for-like categories
- 1 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 Hy4 preview has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex-Mini has a AA Intelligence Index result, 20.4.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
General
Vals Index
No call
Only Hy4 preview has a Vals Index result, 53.2%.
Directional only
Long documents
Listed context window
Hy4 preview
Hy4 preview takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini $0.0013, Hy4 preview $0.0021 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Hy4 preview 44.3 from 3 benchmarks.
- Widest category
- Coding, 56.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GPT-5.1-Codex-Mini 1253 against Hy4 preview 1620, 57.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Hy4 preview $2.501 per million output tokens, 1.3x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Hy4 preview 1.05M.
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-5.1-Codex-Mini | Bars | Hy4 preview | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| All shared boards | 0 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | 1631 | Hy4 preview |
| Terminal-Bench 4 (Vals AI) | Not measured | 8.1% | Only Hy4 preview measured |
| Vibe Code Bench (Vals AI) | Not measured | 77.5% | Only Hy4 preview measured |
| Code migration (Vals AI) | Not measured | 47.4% | Only Hy4 preview measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Hy4 preview measured |
| IOI (Vals AI) | Not measured | 59.3% | Only Hy4 preview measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 55.1% | Only Hy4 preview measured |
| CyberBench (Vals AI) | Not measured | 65.4% | Only Hy4 preview measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: React | Not measured | 1653 | Only Hy4 preview measured |
| LMArena WebDev: HTML | 1253 | 1620 | Hy4 preview |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| SREBench (Vals AI) | Not measured | 2.3% | Only Hy4 preview measured |
| Finance Agent (Vals AI) | Not measured | 55.1% | Only Hy4 preview measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.6% | Only Hy4 preview measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only Hy4 preview measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 8.8% | Only Hy4 preview measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 58.1% | Only Hy4 preview measured |
| BioMysteryBench (Vals AI) | Not measured | 69.3% | Only Hy4 preview measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 1.4% | Only Hy4 preview measured |
| τ²-Bench Telecom | 62.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | Not measured | Only GPT-5.1-Codex-Mini 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-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | Not measured | Only GPT-5.1-Codex-Mini measured |
| AIME 2025 (Artificial Analysis run) | 91.7% | Not measured | Only GPT-5.1-Codex-Mini measured |
| ProofBench | Not measured | 75.0% | Only Hy4 preview measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.8% | Only Hy4 preview measured |
| MedCode (Vals AI) | Not measured | 43.3% | Only Hy4 preview measured |
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | 1078 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | 1092 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | 1097 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1093 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.2% | Only Hy4 preview measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.6% | Only Hy4 preview measured |
| MedScribe (Vals AI) | Not measured | 83.6% | Only Hy4 preview measured |
| IFBench | 67.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | 66.7% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Hy4 preview | 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-5.1-Codex-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Mini 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-5.1-Codex-Mini | Tencent Hy4 preview |
|---|---|---|
| Maker | OpenAI | Tencent |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | August 28, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $0.25 | $0.834 |
| Output $/Mtok | $2.00 | $2.501 |
| Coding index | Not benchmarked | 44.3 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than Hy4 preview for coding?
Only Hy4 preview has a coding score, 44.3 on the Vibe Coding Index; GPT-5.1-Codex-Mini has none, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for output tokens 1.3x cheaper ($2.00 vs $2.501 per million).
Which is cheaper, GPT-5.1-Codex-Mini or Hy4 preview?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Hy4 preview costs $0.834 and $2.501. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Hy4 preview?
Hy4 preview, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Hy4 preview comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Hy4 preview's on 3. Where only one model has a result, the page says so instead of naming a winner.