DeepSeek V4 Flash 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 | DeepSeek-V4-Flash | GLM-5.3 |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | #19 of 2241.8 score | UnrankedNot in the 22-model eligible cohort |
| CostLower is better · Published-token output estimate | #2 of 36$0.0060 per LiveBench case | #28 of 36$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | #1 of 2270.9 score | UnrankedNot in the 22-model eligible cohort |
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 | DeepSeek-V4-Flash | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,431.9897% of row best · rating · deepseek-v4-flash; 95% CI [1427.84091263, 1436.10937297]; votes 48963; rank 80 | 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 | 69.6788% of row best · percent · deepseek-v4-flash · 34,434 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 | DeepSeek-V4-Flash | GLM-5.3 |
|---|---|---|
| Developer | DeepSeek | Z.ai |
| Family | Deepseek V4 Flash | Glm 5 3 |
| Model | DeepSeek-V4-Flash | GLM-5.3 |
| Version | DeepSeek-V4-Flash | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-04-24 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | 290.9B | Unknown |
| Active parameters | 13B | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning | agents, chat, reasoning, structured_outputs, tools |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
DeepSeek V4 Flash Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs | GLM-5.2Z.ai | cross-developer peerstext |
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Claude Fable 5.1Anthropic | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4-ProDeepSeek | family variantstext |
DeepSeek-V4-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4-FlashDeepSeek | cross-developer peerstext |
DeepSeek-V4-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash vs GLM 5.3 FAQs
Is DeepSeek V4 Flash or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash or GLM 5.3?+
DeepSeek V4 Flash is $0.0868 and GLM 5.3 is $1.20 per million tokens, so DeepSeek V4 Flash is cheaper on this metric. DeepSeek V4 Flash is $0.1736 and GLM 5.3 is $4.00 per million tokens, so DeepSeek V4 Flash is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Flash or GLM 5.3?+
DeepSeek V4 Flash has the larger sourced context window. DeepSeek V4 Flash supports 1,049K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, DeepSeek V4 Flash 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 DeepSeek V4 Flash or GLM 5.3 be self-hosted?+
DeepSeek V4 Flash is the only model in this pair currently marked as self-hostable. DeepSeek V4 Flash is open weight; GLM 5.3 is not marked open weight.
Can DeepSeek V4 Flash and GLM 5.3 understand images?+
DeepSeek V4 Flash is not 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, DeepSeek V4 Flash or GLM 5.3?+
Neither has a larger sourced maximum output. DeepSeek V4 Flash is — and GLM 5.3 is 131K.
Do DeepSeek V4 Flash and GLM 5.3 support reasoning and tool use?+
DeepSeek V4 Flash: reasoning. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash or GLM 5.3?+
DeepSeek V4 Flash has 5 sourced provider routes; GLM 5.3 has 4, so DeepSeek V4 Flash has broader tracked availability.
Which offers better value, DeepSeek V4 Flash 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.