GLM 4.7 vs Solar Pro 4
Solar Pro 4 leads GLM 4.7 on the Artificial Analysis Intelligence Index, 28.2 to 22.2, and Solar Pro 4 costs 3.7x less per output token ($0.60 vs $2.20 per million). Updated October 22, 2026; 43 benchmarks have a result for both.
GLM 4.7 or Solar Pro 4: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Solar Pro 4 has none, so the two cannot be ranked on coding.
Pick Solar Pro 4 for output tokens 3.7x cheaper ($0.60 vs $2.20 per million) and a larger context window (524K against 205K tokens).
- Shared results
- 43
- GLM 4.7 only
- 38
- Solar Pro 4 only
- 2
- Like-for-like categories
- 7 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 GLM 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Solar Pro 4
Solar Pro 4 leads, 28.2 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1225 against 1176.
Same board, both measured
Long documents
Listed context window
Solar Pro 4
Solar Pro 4 takes 524K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Solar Pro 4
GLM 4.7 $0.0017, Solar Pro 4 $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Solar Pro 4 not benchmarked.
- Widest category
- Knowledge, 10.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Polish: GLM 4.7 1435 against Solar Pro 4 1304, 21.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Solar Pro 4 $0.60 per million output tokens, 3.7x apart.
- Context window
- GLM 4.7 takes 205K tokens, Solar Pro 4 524K.
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 | GLM 4.7 | Bars | Solar Pro 4 | Ties |
|---|---|---|---|---|
| Coding | 6 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 2 | 3 | ||
| Math | 1 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 6 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 37 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1371 | GLM 4.7 |
| AA Coding Index | 45.3 | 52.7 | Solar Pro 4 |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 48.0% | Only Solar Pro 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | 179.3 | GLM 4.7 |
| LMArena Text: coding | 1454 | 1438 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1431 | GLM 4.7 |
| LMArena WebDev: React | 1420 | 1363 | GLM 4.7 |
| LMArena WebDev: HTML | 1447 | 1371 | GLM 4.7 |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 28.2 | Solar Pro 4 |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1105 | Only Solar Pro 4 measured |
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 89.1% | Solar Pro 4 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 29.8% | Solar Pro 4 |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | 1400 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1428 | Solar Pro 4 |
| LMArena Text: hard prompts in English | 1452 | 1427 | GLM 4.7 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1421 | GLM 4.7 |
| LMArena Text: mathematical work | 1411 | 1427 | Solar Pro 4 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | 1404 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1374 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1383 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1381 | GLM 4.7 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | 1176 | GLM 4.7 |
| Design Arena: UI components | 1196 | 1147 | GLM 4.7 |
| Design Arena: game dev | 1188 | 1169 | GLM 4.7 |
| Design Arena: data viz | 1195 | 1177 | GLM 4.7 |
| Design Arena: 3D | 1190 | 1178 | GLM 4.7 |
| Design Arena: all code categories | 1216 | 1182 | GLM 4.7 |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GLM 4.7 | Solar Pro 4 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | 70.7% | GLM 4.7 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1388 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1375 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1317 | GLM 4.7 |
| LMArena Text: English | 1453 | 1414 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1363 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1422 | GLM 4.7 |
| LMArena Text: German | 1426 | 1358 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1320 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1383 | GLM 4.7 |
| LMArena Text: French | 1432 | 1400 | GLM 4.7 |
| LMArena Text: Spanish | 1434 | 1409 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1357 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1304 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1385 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1382 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1344 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1332 | GLM 4.7 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | Solar Pro 4 | 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
Solar Pro 4 costs less
Repository review
50K input + 3K output tokens
Solar Pro 4 costs less
Agent loop
200K input + 10K output tokens
Solar Pro 4 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 | Upstage Solar Pro 4 |
|---|---|---|
| Maker | Z.ai | Upstage |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | August 10, 2026 |
| Context window | 205K | 524K |
| Input $/Mtok | $0.60 | $0.15 |
| Output $/Mtok | $2.20 | $0.60 |
| Coding index | 55.1 | Not benchmarked |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Solar Pro 4 for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Solar Pro 4 has none, so the two cannot be ranked on coding. Pick Solar Pro 4 for output tokens 3.7x cheaper ($0.60 vs $2.20 per million) and a larger context window (524K against 205K tokens).
Which is cheaper, GLM 4.7 or Solar Pro 4?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Solar Pro 4 costs $0.15 and $0.60. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Solar Pro 4?
Solar Pro 4, with 524K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Solar Pro 4 comparison based on?
43 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Solar Pro 4's on 1. Where only one model has a result, the page says so instead of naming a winner.