GLM 4.6V vs Solar Pro 3
GLM 4.6V leads Solar Pro 3 on the Artificial Analysis Intelligence Index, 11.2 to 7.8, and Solar Pro 3 costs 1.5x less per output token ($0.60 vs $0.90 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GLM 4.6V or Solar Pro 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for work measured by τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 7
- GLM 4.6V only
- 31
- 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 4.6V
GLM 4.6V leads, 11.2 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
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 4.6V $0.0008, Solar Pro 3 $0.0004 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Solar Pro 3 not benchmarked.
- Widest category
- Agentic work, 54.7 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6V 31.6% against Solar Pro 3 86.3%, 54.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Solar Pro 3 $0.60 per million output tokens, 1.5x 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 4.6V | Bars | Solar Pro 3 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 1 | 1 | ||
| All shared boards | 3 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 16.2 | Only Solar Pro 3 measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| SciCode | Not measured | 25.5% | Only Solar Pro 3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 7.6% | GLM 4.6V |
| LMArena Text: coding | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: software and IT | 1391 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 7.8 | GLM 4.6V |
| 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 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.4 | Only Solar Pro 3 measured |
| τ²-Bench Telecom | 31.6% | 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 index02
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 72.4% | Solar Pro 3 |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 10.3% | Solar Pro 3 |
| LMArena Text: hard prompts | 1367 | Not measured | Only GLM 4.6V measured |
| LMArena Text: expert | 1371 | Not measured | Only GLM 4.6V measured |
| LMArena Text: hard prompts in English | 1391 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: medicine and healthcare | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: legal and government | 1393 | Not measured | Only GLM 4.6V measured |
| LMArena Text: business and finance | 1359 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.6V | Solar Pro 3 | Leader |
|---|---|---|---|
| IFBench | 30.1% | 71.2% | Solar Pro 3 |
| Long context reasoning (AA-LCR) | 48.7% | 32.3% | GLM 4.6V |
| LMArena Text | 1376 | Not measured | Only GLM 4.6V measured |
| LMArena Text: instruction following | 1350 | Not measured | Only GLM 4.6V measured |
| LMArena Text: creative writing | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | Not measured | Only GLM 4.6V measured |
| LMArena Text: non-English | 1358 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Chinese | 1425 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Russian | 1339 | Not measured | Only GLM 4.6V measured |
| LMArena Text: multi-turn | 1359 | Not measured | Only GLM 4.6V measured |
| LMArena Text: longer queries | 1357 | Not measured | Only GLM 4.6V measured |
| LMArena Text: writing and language | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Text: entertainment and media | 1343 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | 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 4.6V | Upstage Solar Pro 3 |
|---|---|---|
| Maker | Z.ai | Upstage |
| Weights | Open weights | No public weights listed |
| Released | December 8, 2025 | January 27, 2026 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.30 | $0.15 |
| Output $/Mtok | $0.90 | $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 4.6V better than Solar Pro 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for work measured by τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.6V or Solar Pro 3?
GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or Solar Pro 3?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.6V vs Solar Pro 3 comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V'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.