Qwen3.8 Max vs GLM 5
At a Glance
| Compare |  Qwen3.8 MaxQwen |  GLM 5Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #10 of 4679.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 52.8–86.1 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #23 of 44$0.107 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #6 of 3863.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 50.0–66.7 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.65Deepinfra ↗ · Sep 22, 2026 | $0.60Deepinfra ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $4.951Deepinfra ↗ · Sep 22, 2026 | $1.92Openrouter ↗ · Aug 28, 2026 |
| Context windowMaximum documented tokens | 1,000K | 203K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 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-Max | GLM-5 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,480.55100% of row best · rating · qwen3.8-max; 95% CI [1474.79040793, 1486.31846927]; votes 16670; rank 14 | 1,446.2798% of row best · rating · glm-5; 95% CI [1441.95071870, 1450.59229773]; votes 27605; rank 51 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 83.32100% of row best · points · Qwen3.8 Max · 22,888 output tokens / case | 82.8999% of row best · points · GLM-5 · 15,496 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winNo overall winner | 0 benchmark winsNo 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 | Qwen3.8-Max | GLM-5 |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 8 Max | Glm 5 |
| Model | Qwen3.8-Max | GLM-5 |
| Version | Qwen3.8-Max | GLM-5 |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 203K |
| Total parameters | 2.4T | 753.9B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | agents, chat, reasoning, structured_outputs, tools, vision | chat, generation, reasoning, tools |
Qwen3.8 Max Capabilities
GLM 5 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
 GPT-6 AstraOpenAI | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Qwen3.8-MaxQwen | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Qwen3.8-MaxQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
 Kimi-K3Moonshot AI | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Qwen3.8-MaxQwen | vs | GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.8-MaxQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text, video |
 Mistral Large 3Mistral AI | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
| vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 Max vs GLM 5 FAQs
Is Qwen3.8 Max or GLM 5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 Max and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 Max or GLM 5?+
Qwen3.8 Max is $1.65 and GLM 5 is $0.60 per million tokens, so GLM 5 is cheaper on this metric. Qwen3.8 Max is $4.951 and GLM 5 is $1.92 per million tokens, so GLM 5 is cheaper on this metric.
Which has a larger context window, Qwen3.8 Max or GLM 5?+
Qwen3.8 Max has the larger sourced context window. Qwen3.8 Max supports 1,000K and GLM 5 supports 203K.
Which performs better in benchmarks, Qwen3.8 Max or GLM 5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.8 Max or GLM 5 be self-hosted?+
GLM 5 is the only model in this pair currently marked as self-hostable. Qwen3.8 Max is not marked open weight; GLM 5 is open weight.
Can Qwen3.8 Max and GLM 5 understand images?+
Qwen3.8 Max is documented with image input; GLM 5 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 Max or GLM 5?+
Neither has a larger sourced maximum output. Qwen3.8 Max is 131K and GLM 5 is —.
Do Qwen3.8 Max and GLM 5 support reasoning and tool use?+
Qwen3.8 Max: reasoning, tool calling, and image input. GLM 5: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 Max or GLM 5?+
Qwen3.8 Max has 4 sourced provider routes; GLM 5 has 4, a tie.
Which offers better value, Qwen3.8 Max or GLM 5?+
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.