Compare / Qwen3-30B-A3B-Instruct-2507 vs GLM-5.1
Qwen3-30B-A3B-Instruct-2507 vs GLM-5.1
Which is cheaper, which is more capable, and which does better at each kind of work, from 16 results on 3 sources that measured both models.
Of the 16 results both models have, the better value on each. Results whose reported ranges overlap are too close to call.
Results as of 9 October 2026 (catalogue release 2026-10-09-dd620e778ac5). See also Alibaba vs Z.ai, every model from both labs.
Qwen3-30B-A3B-Instruct-2507 or GLM-5.1? The short answer
Pick GLM-5.1 if quality matters most: it scores higher (Intelligence Index 26.1 against 7.5). Pick Qwen3-30B-A3B-Instruct-2507 if cost matters more: it costs 15 times less per token.
- Qwen3-30B-A3B-Instruct-2507 is cheaper: $0.14 against $2.13 per million tokens, 15 times less.
- GLM-5.1 scores higher on the Artificial Analysis Intelligence Index: 26.1 against 7.5.
- GLM-5.1 is newer: released 7 Apr 2026, against 29 Jul 2025 for Qwen3-30B-A3B-Instruct-2507.
- On third-party benchmarks, on the results both models have: GLM-5.1 does better for reasoning and knowledge and writing.
Head to head
Every fact side by side
| Qwen3-30B-A3B-Instruct-2507 | GLM-5.1 | |
|---|---|---|
| Price per million tokens | $0.090 in · $0.30 out | $1.38 in · $4.40 out |
| Blended (3 in : 1 out) | $0.14 | $2.13 |
| Cheapest host, blended | $0.084 (StreamLake) | $1.48 (StreamLake, FP8) |
| Intelligence Index | 7.5 | 26.1 |
| Spring Prompt overall | – | – |
| Output speed, tokens a second | – | – |
| Context window | 262,144 tokens | 204,800 tokens |
| Released | 29 Jul 2025 | 7 Apr 2026 (newer) |
| Weights | Open | Open |
| Developer based in | China | China |
Prices are the developer's list price where we have read it, otherwise the typical host on OpenRouter; see every model's price. Intelligence and speed from Artificial Analysis.
Which is better for what
Each model's rank among the models on sale today, for each kind of work, from our own benchmarks and licensed sources. Further right is better; the better rank is in green.
Every shared result
Each model's best result on every measure both have, grouped by benchmark, with the setting that produced it. Under each measure, every model that benchmark has measured is a grey tick, best to the right, so you can see where the two sit in the field. The headline measures of each benchmark come first; the rest are under "Show all". Green marks the better value; where a source reports ranges that overlap, neither is marked.
| Measure | Qwen3-30B-A3B-Instruct-2507 | GLM-5.1 |
|---|---|---|
| Arena (formerly LMArena) reported by Arena (formerly LMArena) · 3 results | ||
| Overall · TextArena rating, higher is better 162 · 47 of 387 measured | 1,383 | 1,465 |
| Business, management and finance · TextArena rating, higher is better 144 · 43 of 380 measured | 1,397 | 1,465 |
| Creative writing · TextArena rating, higher is better 186 · 37 of 385 measured | 1,319 | 1,446 |
| Artificial Analysis reported by Artificial Analysis · 3 results | ||
| Artificial Analysis Intelligence Indexindex score, higher is better 209 · 66 of 243 measured | 7.5 | 26.1 |
| GPQA Diamond% of questions, higher is better 172 · 67 of 231 measured | 65.9% | 86.8% |
| Humanity's Last Exam% of questions, higher is better 173 · 72 of 242 measured | 6.9% | 30.1% |
| UGI Leaderboard reported by UGI Leaderboard · 3 results | ||
| Writing score · UGIscore out of 100, higher is better 163 · 39 of 230 measured | 27.9 | 61.7thinking reasoning |
| Requested-length error · UGI% off the requested word count, lower is better 57 · 33 of 230 measured | 13.0% | 9.0%thinking reasoning |
| Style adherence · UGIscore from 0 to 1, higher is better 187 · 27 of 230 measured | 0.31 | 0.38thinking reasoning |
Show all 16 results (7 more)
| Measure | Qwen3-30B-A3B-Instruct-2507 | GLM-5.1 |
|---|---|---|
| Arena (formerly LMArena) reported by Arena (formerly LMArena) · 3 results | ||
| Expert prompts · TextArena rating, higher is better 163 · 46 of 338 measured | 1,395 | 1,494 |
| Instruction following · TextArena rating, higher is better 164 · 42 of 387 measured | 1,367 | 1,460 |
| Writing, literature and language · TextArena rating, higher is better 176 · 42 of 386 measured | 1,342 | 1,452 |
| Artificial Analysis reported by Artificial Analysis · 4 results | ||
| IFBench% of instructions, higher is better 168 · 17 of 195 measured | 33.1% | 76.3% |
| Long-context reasoning (AA-LCR)% of questions, higher is better 191 · 90 of 233 measured | 26.3% | 73.7% |
| Terminal-Bench Hard% of tasks, higher is better 147 · 26 of 190 measured | 6.1% | 43.2% |
| τ²-bench telecom% of tasks, higher is better 185 · 7 of 193 measured | 10.2% | 97.7% |
Compare them with other models
Quick answers
Is Qwen3-30B-A3B-Instruct-2507 better than GLM-5.1?
Pick GLM-5.1 if quality matters most: it scores higher (Intelligence Index 26.1 against 7.5). Pick Qwen3-30B-A3B-Instruct-2507 if cost matters more: it costs 15 times less per token.
Which is cheaper, Qwen3-30B-A3B-Instruct-2507 or GLM-5.1?
Qwen3-30B-A3B-Instruct-2507 is cheaper: $0.14 against $2.13 per million tokens, 15 times less.
Which is better for agents and tool use, Qwen3-30B-A3B-Instruct-2507 or GLM-5.1?
GLM-5.1 ranks 58 of 164 models on sale for multi-step tasks with tools: support desks, coding agents, function calls, against 75 for Qwen3-30B-A3B-Instruct-2507.
Which is better for reasoning and knowledge, Qwen3-30B-A3B-Instruct-2507 or GLM-5.1?
GLM-5.1 ranks 66 of 198 models on sale for hard questions across science, maths and general knowledge, against 159 for Qwen3-30B-A3B-Instruct-2507.
Which is better for writing, Qwen3-30B-A3B-Instruct-2507 or GLM-5.1?
GLM-5.1 ranks 38 of 188 models on sale for what people prefer in blind comparisons, and judged writing quality, against 140 for Qwen3-30B-A3B-Instruct-2507.
Which has the bigger context window, Qwen3-30B-A3B-Instruct-2507 or GLM-5.1?
Qwen3-30B-A3B-Instruct-2507: 262,144 tokens, against 204,800 for GLM-5.1.
Run Qwen3-30B-A3B-Instruct-2507 and GLM-5.1 on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.