GLM 4.5 Air vs Solar Pro 3
GLM 4.5 Air leads Solar Pro 3 on the Artificial Analysis Intelligence Index, 11.1 to 7.8, and Solar Pro 3 costs 1.8x less per output token ($0.60 vs $1.10 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GLM 4.5 Air or Solar Pro 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for work measured by τ²-Bench Telecom or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 7
- GLM 4.5 Air only
- 41
- Solar Pro 3 only
- 3
- Like-for-like categories
- 5 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5 Air
GLM 4.5 Air leads, 11.1 against 7.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Solar Pro 3 has a AA Agentic Index result, 1.4.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.5 Air has a Design Arena: websites result, 1146.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Solar Pro 3
GLM 4.5 Air $0.0008, Solar Pro 3 $0.0004 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 Air not benchmarked, Solar Pro 3 not benchmarked.
- Widest category
- Agentic work, 39.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 Air 46.5% against Solar Pro 3 86.3%, 39.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 Air $1.10 against Solar Pro 3 $0.60 per million output tokens, 1.8x apart.
- Context window
- Both take 131K tokens.
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 4.5 Air | Bars | Solar Pro 3 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| General | 1 | 1 | ||
| All shared boards | 4 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 16.2 | Only Solar Pro 3 measured |
| LiveCodeBench (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5 Air measured |
| SciCode | Not measured | 25.5% | Only Solar Pro 3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 20.5% | 7.6% | GLM 4.5 Air |
| GSO-Bench | 2.9% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: coding | 1397 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: software and IT | 1401 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 7.8 | GLM 4.5 Air |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.4 | Only Solar Pro 3 measured |
| τ²-Bench Telecom | 46.5% | 86.3% | Solar Pro 3 |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 73.3% | 72.4% | GLM 4.5 Air |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 10.3% | Solar Pro 3 |
| LMArena Text: hard prompts | 1379 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: expert | 1370 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: hard prompts in English | 1388 | Not measured | Only GLM 4.5 Air measured |
| Kagi LLM Benchmark | 43.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Math Index | 80.7 | Not measured | Only GLM 4.5 Air measured |
| AIME 2025 (Artificial Analysis run) | 80.7% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: math | 1396 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: mathematical work | 1396 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.5% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: science | 1407 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: medicine and healthcare | 1402 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: legal and government | 1382 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: business and finance | 1371 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1146 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: UI components | 1120 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: game dev | 1094 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: data viz | 1186 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: 3D | 1122 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: all code categories | 1137 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: SVG | 1071 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.5 Air | Solar Pro 3 | Leader |
|---|---|---|---|
| IFBench | 37.6% | 71.2% | Solar Pro 3 |
| Long context reasoning (AA-LCR) | 46.7% | 32.3% | GLM 4.5 Air |
| LMArena Text | 1384 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: instruction following | 1354 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: creative writing | 1343 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: English | 1397 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: non-English | 1366 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Chinese | 1426 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: German | 1377 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Japanese | 1348 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Korean | 1308 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: French | 1400 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Spanish | 1385 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Russian | 1373 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Polish | 1368 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: multi-turn | 1371 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: longer queries | 1366 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: writing and language | 1354 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: entertainment and media | 1339 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 Air | Solar Pro 3 | 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 3 costs less
Repository review
50K input + 3K output tokens
Solar Pro 3 costs less
Agent loop
200K input + 10K output tokens
Solar Pro 3 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 4.5 Air | Upstage Solar Pro 3 |
|---|---|---|
| Maker | Z.ai | Upstage |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | January 27, 2026 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.20 | $0.15 |
| Output $/Mtok | $1.10 | $0.60 |
| 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 4.5 Air better than Solar Pro 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for work measured by τ²-Bench Telecom or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5 Air or Solar Pro 3?
GLM 4.5 Air costs $0.20 per million input tokens and $1.10 per million output tokens; Solar Pro 3 costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 Air or Solar Pro 3?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.5 Air vs Solar Pro 3 comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 independent benchmarks and Solar Pro 3's on 0. Where only one model has a result, the page says so instead of naming a winner.