Qwen3.8 27B vs GLM 5.3 Flash
At a Glance
| Compare |  Qwen3.8 27BQwen |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4 | #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 | #16 of 44$0.072 per LiveBench case | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5 | #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 | $0.20Deepinfra ↗ · Sep 22, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Deepinfra ↗ · Sep 22, 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
| Benchmark | Qwen3.8-27B | GLM-5.3-Flash |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -0.6498% of row best · score · Qwen 3.8 27B; 95% CI [-1.36495564, 0.07819043]; sessions 37257; observations 4349219; rank 29 | 1.15100% of row best · score · GLM 5.3 Flash; 95% CI [0.48275443, 1.81278415]; sessions 43164; observations 4433017; rank 25 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,439.2798% of row best · rating · qwen3.8-27b; 95% CI [1432.84410050, 1445.69542028]; votes 10697; rank 68 | 1,471.89100% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,272.1298% of row best · rating · qwen3.8-27b; 95% CI [1262.21853873, 1282.01179703]; votes 4577; rank 35 | 1,298.71100% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17 |
| LiveBench2026-06-25 · overall · leader | 78.02100% of row best · percent · qwen3.8-27b · 28,740 output tokens / case | 73.2794% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark win | 3 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | Qwen3.8-27B | GLM-5.3-Flash |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 8 27b | Glm 5 3 Flash |
| Model | Qwen3.8-27B | GLM-5.3-Flash |
| Version | Qwen3.8-27B | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 27.8B | 320B |
| Active parameters | Unknown | 18B |
| License | apache-2.0 | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (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 27B Capabilities
GLM 5.3 Flash Capabilities
Related Comparisons
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|---|---|---|---|
Qwen3.8-27BQwen | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.8-27BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.8-27BQwen | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.8-27BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 27B vs GLM 5.3 Flash FAQs
Is Qwen3.8 27B or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B 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 27B or GLM 5.3 Flash?+
Qwen3.8 27B is $0.20 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Qwen3.8 27B is $2.50 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 27B or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced context window. Qwen3.8 27B supports 262K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Qwen3.8 27B or GLM 5.3 Flash?+
GLM 5.3 Flash leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Qwen3.8 27B or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 27B is open weight; GLM 5.3 Flash is open weight.
Can Qwen3.8 27B and GLM 5.3 Flash understand images?+
Qwen3.8 27B is 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 27B or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and GLM 5.3 Flash is 131K.
Do Qwen3.8 27B and GLM 5.3 Flash support reasoning and tool use?+
Qwen3.8 27B: reasoning, tool calling, and image input. 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 27B or GLM 5.3 Flash?+
Qwen3.8 27B has 3 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, Qwen3.8 27B 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.