GPT-5.1-Codex vs Morph V3 Fast
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- 15.3
- Overall
- 23.7
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.20
Decision reading
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Morph V3 Fast has none, so the two cannot be ranked on coding.
Morph V3 Fast has the edge where output tokens cost 8.3x less.
No benchmark has tested both yet. GPT-5.1-Codex has results on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Vals AI); Morph V3 Fast has none yet. Compare them on price and context instead.
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-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex has a AA Intelligence Index result, 23.7.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex has a Design Arena: websites result, 1162.
Directional only
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Morph V3 Fast
GPT-5.1-Codex $0.0063, Morph V3 Fast $0.0014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, Morph V3 Fast not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.1-Codex $10.00 against Morph V3 Fast $1.20 per million output tokens, 8.3x apart.
- Context window
- GPT-5.1-Codex takes 400K tokens, Morph V3 Fast 262K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1337 | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Vals AI) | 85.5% | Not measured | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | 13.1% | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| ALE-Bench | 1244.9 | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev: HTML | 1346 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 83.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: UI components | 1237 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: game dev | 1138 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: data viz | 1213 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: all code categories | 1152 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex | Morph V3 Fast | Leader |
|---|---|---|---|
| IFBench | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 44 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
Morph V3 Fast costs less
Repository review
50K input + 3K output tokens
Morph V3 Fast costs less
Agent loop
200K input + 10K output tokens
Morph V3 Fast 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 | Morph V3 Fast |
|---|---|---|
| Maker | OpenAI | Morph |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | July 7, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.80 |
| Output $/Mtok | $10.00 | $1.20 |
| Coding index | 15.3 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.892 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than Morph V3 Fast for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Morph V3 Fast has none, so the two cannot be ranked on coding. Morph V3 Fast has the edge where output tokens cost 8.3x less.
Which is cheaper, GPT-5.1-Codex or Morph V3 Fast?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; Morph V3 Fast costs $0.80 and $1.20. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, GPT-5.1-Codex or Morph V3 Fast?
GPT-5.1-Codex, with 400K tokens against 262K.
Has any benchmark tested both GPT-5.1-Codex and Morph V3 Fast?
No benchmark has tested both yet. GPT-5.1-Codex has results on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Vals AI); Morph V3 Fast has none yet. Compare them on price and context instead. Data as of October 21, 2026.