GPT-5.5 Pro vs Morph V3 Large
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $180.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.90
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-5.5 Pro has the edge where the context window is larger, 1.05M against 262K tokens.
- Shared results
- 0
- GPT-5.5 Pro only
- 9
- Morph V3 Large only
- 0
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Long documents
Listed context window
GPT-5.5 Pro
GPT-5.5 Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Morph V3 Large
GPT-5.5 Pro $0.120, Morph V3 Large $0.0019 per turn.
List prices
At a glance
- Coding index
- GPT-5.5 Pro not benchmarked, Morph V3 Large not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- GPT-5.5 Pro $180.00 against Morph V3 Large $1.90 per million output tokens, 94.7x apart.
- Context window
- GPT-5.5 Pro takes 1.05M tokens, Morph V3 Large 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.
CodingNot comparableSix of these boards feed the Vibe Coding Index0 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| Neither model has a result on 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 162.9 | Not measured | Only GPT-5.5 Pro measured |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| ARC-AGI | 96.5% | Not measured | Only GPT-5.5 Pro measured |
| ARC-AGI-2 | 84.6% | Not measured | Only GPT-5.5 Pro measured |
| SimpleBench | 76.9% | Not measured | Only GPT-5.5 Pro measured |
| Chess puzzles | 62.0% | Not measured | Only GPT-5.5 Pro measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| FrontierMath, tiers 1 to 3 | 87.7% | Not measured | Only GPT-5.5 Pro measured |
| FrontierMath, tier 4 | 78.0% | Not measured | Only GPT-5.5 Pro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 Pro | Morph V3 Large | 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
Morph V3 Large costs less
Repository review
50K input + 3K output tokens
Morph V3 Large costs less
Agent loop
200K input + 10K output tokens
Morph V3 Large 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-5.5 Pro | Morph V3 Large |
|---|---|---|
| Maker | OpenAI | Morph |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | July 7, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $30.00 | $0.90 |
| Output $/Mtok | $180.00 | $1.90 |
| 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-5.5 Pro better than Morph V3 Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5.5 Pro has the edge where the context window is larger, 1.05M against 262K tokens.
Which is cheaper, GPT-5.5 Pro or Morph V3 Large?
GPT-5.5 Pro costs $30.00 per million input tokens and $180.00 per million output tokens; Morph V3 Large costs $0.90 and $1.90. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.5 Pro or Morph V3 Large?
GPT-5.5 Pro, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.5 Pro vs Morph V3 Large comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. GPT-5.5 Pro's coding index rests on 0 independent benchmarks and Morph V3 Large's on 0. Where only one model has a result, the page says so instead of naming a winner.