Qwen3.5 35B A3B vs GLM 5.3
At a Glance
| Compare |  Qwen3.5 35B A3BQwen |  GLM 5.3Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #34 of 44$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 22, 2026 | $1.20Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 22, 2026 | $4.00Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Qwen3.5-35B-A3B | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,395.5095% of row best · rating · qwen3.5-35b-a3b; 95% CI [1391.20067764, 1399.79162231]; votes 29043; rank 148 | 1,475.08100% of row best · rating · glm-5.3-max; 95% CI [1468.67816284, 1481.48915679]; votes 10960; rank 20 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Qwen3.5-35B-A3B | GLM-5.3 |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 5 35b A3b | Glm 5 3 |
| Model | Qwen3.5-35B-A3B | GLM-5.3 |
| Version | Qwen3.5-35B-A3B | GLM-5.3 |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 36B | Unknown |
| Active parameters | 3B | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, reasoning, structured_outputs, tools |
Qwen3.5 35B A3B Capabilities
GLM 5.3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3.5-35B-A3BQwen | vs | GLM-5.2Z.ai | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.5-35B-A3BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.5-35B-A3BQwen | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 35B A3B vs GLM 5.3 FAQs
Is Qwen3.5 35B A3B or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 35B A3B or GLM 5.3?+
Qwen3.5 35B A3B is $0.14 and GLM 5.3 is $1.20 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric. Qwen3.5 35B A3B is $1.00 and GLM 5.3 is $4.00 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric.
Which has a larger context window, Qwen3.5 35B A3B or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Qwen3.5 35B A3B supports 262K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Qwen3.5 35B A3B or GLM 5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 35B A3B or GLM 5.3 be self-hosted?+
Qwen3.5 35B A3B is the only model in this pair currently marked as self-hostable. Qwen3.5 35B A3B is open weight; GLM 5.3 is not marked open weight.
Can Qwen3.5 35B A3B and GLM 5.3 understand images?+
Qwen3.5 35B A3B is documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 35B A3B or GLM 5.3?+
Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and GLM 5.3 is 131K.
Do Qwen3.5 35B A3B and GLM 5.3 support reasoning and tool use?+
Qwen3.5 35B A3B: reasoning, tool calling, and image input. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 35B A3B or GLM 5.3?+
Qwen3.5 35B A3B has 4 sourced provider routes; GLM 5.3 has 4, a tie.
Which offers better value, Qwen3.5 35B A3B or GLM 5.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.