Qwen3.8 2.4T A95B vs GLM 5.3 Flash
At a Glance
| Compare |  GLM 5.3 FlashZ.ai | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Deepinfra ↗ · Sep 23, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $6.00Deepinfra ↗ · Sep 23, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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
Side-by-Side Facts
| Field | Qwen3.8-2.4T-A95B | GLM-5.3-Flash |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 8 2 4t A95b | Glm 5 3 Flash |
| Model | Qwen3.8-2.4T-A95B | GLM-5.3-Flash |
| Version | Qwen3.8-2.4T-A95B | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 2.4T | 320B |
| Active parameters | 95B | 18B |
| License | other | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (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, computer-use, reasoning, structured_outputs, tools, vision |
Qwen3.8 2.4T A95B Capabilities
GLM 5.3 Flash Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs |  GPT-6 AstraOpenAI | cross-developer peerstext | |
| vs | Claude Fable 5.1Anthropic | cross-developer peerstext | |
| vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext | |
| vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | family variantstext | |
| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs | GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
| vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext | |
| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 2.4T A95B vs GLM 5.3 Flash FAQs
Is Qwen3.8 2.4T A95B or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 2.4T A95B and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
Qwen3.8 2.4T A95B is $2.00 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Qwen3.8 2.4T A95B is $6.00 and GLM 5.3 Flash is $0.25 per million tokens, so GLM 5.3 Flash is cheaper on this metric.
Which has a larger context window, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced context window. Qwen3.8 2.4T A95B supports 262K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.8 2.4T A95B or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 2.4T A95B is open weight; GLM 5.3 Flash is open weight.
Can Qwen3.8 2.4T A95B and GLM 5.3 Flash understand images?+
Qwen3.8 2.4T A95B is not documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. Qwen3.8 2.4T A95B is — and GLM 5.3 Flash is 131K.
Do Qwen3.8 2.4T A95B and GLM 5.3 Flash support reasoning and tool use?+
Qwen3.8 2.4T A95B: reasoning and tool calling. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
Qwen3.8 2.4T A95B has 5 sourced provider routes; GLM 5.3 Flash has 4, so Qwen3.8 2.4T A95B has broader tracked availability.
Which offers better value, Qwen3.8 2.4T A95B or GLM 5.3 Flash?+
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.