GLM 4.7 Flash vs GPT-5.5 Pro
GLM 4.7 Flash costs 450.0x less per output token than GPT-5.5 Pro ($0.40 vs $180.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 42.1
- Output $/M
- $0.40
- Coding
- No data
- Overall
- No data
- Output $/M
- $180.00
GLM 4.7 Flash or GPT-5.5 Pro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 450.0x cheaper ($0.40 vs $180.00 per million).
No benchmark has tested both yet. GLM 4.7 Flash has results on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; GPT-5.5 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.7 Flash has a AA Intelligence Index result, 14.9.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
GPT-5.5 Pro
GPT-5.5 Pro takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, GPT-5.5 Pro $0.120 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, GPT-5.5 Pro 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
- GLM 4.7 Flash $0.40 against GPT-5.5 Pro $180.00 per million output tokens, 450.0x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, GPT-5.5 Pro 1.05M.
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 Index3 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | 1389 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | Not measured | Only GLM 4.7 Flash measured |
| Epoch Capabilities Index | Not measured | 162.9 | Only GPT-5.5 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | Not measured | Only GLM 4.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | Not measured | Only GLM 4.7 Flash measured |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| ARC-AGI | Not measured | 96.5% | Only GPT-5.5 Pro measured |
| ARC-AGI-2 | Not measured | 84.6% | Only GPT-5.5 Pro measured |
| SimpleBench | Not measured | 76.9% | Only GPT-5.5 Pro measured |
| Chess puzzles | Not measured | 62.0% | Only GPT-5.5 Pro measured |
| LMArena Text: hard prompts | 1356 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: expert | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 87.7% | Only GPT-5.5 Pro measured |
| FrontierMath, tier 4 | Not measured | 78.0% | Only GPT-5.5 Pro measured |
| LMArena Text: math | 1355 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| LMArena Text: science | 1358 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | 1343 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | 1343 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1208 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1135 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: SVG | 1038 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards measured
| Benchmark | GLM 4.7 Flash | GPT-5.5 Pro | Leader |
|---|---|---|---|
| IFBench | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | Not measured | Only GLM 4.7 Flash measured |
| DTBench | Not measured | 96.0% | Only GPT-5.5 Pro measured |
| LMCA | Not measured | 53.9% | Only GPT-5.5 Pro measured |
| LMArena Text | 1351 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | 1327 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | 1296 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: English | 1373 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | 1330 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | 1403 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | 1350 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | 1333 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | 1345 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | 1320 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | 1304 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 26 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
GLM 4.7 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 Flash 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 | Z.ai GLM 4.7 Flash | OpenAI GPT-5.5 Pro |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | April 24, 2026 |
| Context window | 200K | 1.05M |
| Input $/Mtok | $0.0653 | $30.00 |
| Output $/Mtok | $0.40 | $180.00 |
| 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 GLM 4.7 Flash better than GPT-5.5 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 450.0x cheaper ($0.40 vs $180.00 per million).
Which is cheaper, GLM 4.7 Flash or GPT-5.5 Pro?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GPT-5.5 Pro costs $30.00 and $180.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or GPT-5.5 Pro?
GPT-5.5 Pro, with 1.05M tokens against 200K.
Has any benchmark tested both GLM 4.7 Flash and GPT-5.5 Pro?
No benchmark has tested both yet. GLM 4.7 Flash has results on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; GPT-5.5 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. Compare them on price and context instead. Data as of October 21, 2026.