Qwen3.8 27B vs GLM 5.3
Market Position
LiveBench Quality Versus Estimated Output Cost
This current-market view appears only when both compared models are reviewed current models with publisher-reported LiveBench token accounting and current sourced USD output rates.
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Qwen3.8-27B | GLM-5.3 |
|---|---|---|
| CostLower is better · Published-token output estimate | #16 of 36$0.086 per LiveBench case | #28 of 36$0.248 per LiveBench case |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | Qwen3.8-27B | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,437.3597% of row best · rating · qwen3.8-27b; 95% CI [1429.71618499, 1444.98971204]; votes 6626; rank 69 | 1,474.49100% of row best · rating · glm-5.3-max; 95% CI [1467.12172899, 1481.86064533]; votes 7401; rank 18 |
| LiveBench2026-06-25 · overall · leader | 78.0299% of row best · percent · qwen3.8-27b · 28,740 output tokens / case | 79.15100% of row best · percent · glm-5.3 · 62,090 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 2 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.
Technical Differences
Side-by-Side Facts
| Field | Qwen3.8-27B | GLM-5.3 |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 8 27b | Glm 5 3 |
| Model | Qwen3.8-27B | GLM-5.3 |
| Version | Qwen3.8-27B | 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 | 27.8B | Unknown |
| Active parameters | Unknown | 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) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, reasoning, structured_outputs, tools |
13 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Qwen3.8 27B Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
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Qwen3.8-27BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  DeepSeek-V4-ProDeepSeek | cross-developer peerstext |
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 |  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 |
Qwen3.8-27BQwen | vs | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 27B vs GLM 5.3 FAQs
Is Qwen3.8 27B or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and GLM 5.3, 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?+
Qwen3.8 27B is $0.40 and GLM 5.3 is $1.20 per million tokens, so Qwen3.8 27B is cheaper on this metric. Qwen3.8 27B is $3.00 and GLM 5.3 is $4.00 per million tokens, so Qwen3.8 27B is cheaper on this metric.
Which has a larger context window, Qwen3.8 27B or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Qwen3.8 27B supports 262K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Qwen3.8 27B or GLM 5.3?+
GLM 5.3 leads the current overall benchmark count. The result uses 2 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Qwen3.8 27B or GLM 5.3 be self-hosted?+
Qwen3.8 27B is the only model in this pair currently marked as self-hostable. Qwen3.8 27B is open weight; GLM 5.3 is not marked open weight.
Can Qwen3.8 27B and GLM 5.3 understand images?+
Qwen3.8 27B 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, Qwen3.8 27B or GLM 5.3?+
Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and GLM 5.3 is 131K.
Do Qwen3.8 27B and GLM 5.3 support reasoning and tool use?+
Qwen3.8 27B: 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, Qwen3.8 27B or GLM 5.3?+
Qwen3.8 27B has 3 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Qwen3.8 27B 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.