DeepSeek V4.1 Flash vs GLM 5.2
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  GLM 5.2Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #23 of 4661.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.3–74.6 |
| CostLower is better · Published-token output estimate | #5 of 44$0.022 per LiveBench case | #12 of 44$0.056 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #7 of 3861.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 51.0–67.6 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.15DeepSeek ↗ · Sep 10, 2026 | $0.75Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.60Deepinfra ↗ · Sep 22, 2026 | $2.40Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Sep 10, 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 | DeepSeek-V4.1-Flash | GLM-5.2 |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · statistical tie | 4.88100% of row best · score · Deepseek V4.1 Flash (Max); 95% CI [3.54852933, 6.21109128]; sessions 20080; observations 2295632; rank 12 | 4.37100% of row best · score · GLM 5.2 (Max); 95% CI [3.67608698, 5.05982987]; sessions 76766; observations 4943866; rank 14 |
| LiveBench2026-06-25 · overall · leader | 83.20100% of row best · percent · deepseek-v4.1-flash-max · 36,355 output tokens / case | 76.9692% of row best · percent · glm-5.2 · 23,463 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 88.03100% of row best · points · DeepSeek V4.1 Flash (max) · 18,199 output tokens / case | 82.1493% of row best · points · GLM-5.2 · 5,633 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 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 | DeepSeek-V4.1-Flash | GLM-5.2 |
|---|---|---|
| Developer | DeepSeek | Z.ai |
| Family | Deepseek V4 1 | Glm 5 2 |
| Model | DeepSeek-V4.1-Flash | GLM-5.2 |
| Version | DeepSeek-V4.1-Flash | GLM-5.2 |
| Lifecycle | active | active |
| Released | 2026-09-10 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | 763.2B | 753.3B |
| Active parameters | Unknown | Unknown |
| License | mit | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | chat, generation, reasoning, tools |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| Transformer layers | 40 layers | Unknown |
DeepSeek V4.1 Flash Capabilities
GLM 5.2 Capabilities
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V4.1 Flash vs GLM 5.2 FAQs
Is DeepSeek V4.1 Flash or GLM 5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and GLM 5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or GLM 5.2?+
DeepSeek V4.1 Flash is $0.15 and GLM 5.2 is $0.75 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric. DeepSeek V4.1 Flash is $0.60 and GLM 5.2 is $2.40 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric.
Which has a larger context window, DeepSeek V4.1 Flash or GLM 5.2?+
Neither model has a larger sourced context window in this comparison. DeepSeek V4.1 Flash is 1,049K and GLM 5.2 is 1,049K.
Which performs better in benchmarks, DeepSeek V4.1 Flash or GLM 5.2?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can DeepSeek V4.1 Flash or GLM 5.2 be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4.1 Flash is open weight; GLM 5.2 is open weight.
Can DeepSeek V4.1 Flash and GLM 5.2 understand images?+
DeepSeek V4.1 Flash is documented with image input; GLM 5.2 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or GLM 5.2?+
Neither has a larger sourced maximum output. DeepSeek V4.1 Flash is 393K and GLM 5.2 is —.
Do DeepSeek V4.1 Flash and GLM 5.2 support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or GLM 5.2?+
DeepSeek V4.1 Flash has 3 sourced provider routes; GLM 5.2 has 5, so GLM 5.2 has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash or GLM 5.2?+
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.