DeepSeek V4 Pro vs GLM 5.3 Flash
At a Glance
| Compare |  DeepSeek V4 ProDeepSeek |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #28 of 4655.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.9–70.2 | #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 | #10 of 44$0.054 per LiveBench case | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #12 of 3858.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 49.2–65.9 | #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 | $0.66DeepSeek ↗ · Sep 2, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $1.5441Openrouter ↗ · Aug 28, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 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 | DeepSeek-V4-Pro | GLM-5.3-Flash |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -0.7198% of row best · score · DeepSeek V4 Pro; 95% CI [-1.64679871, 0.21730076]; sessions 35465; observations 1910125; rank 30 | 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 | 1,450.6399% of row best · rating · deepseek-v4-pro; 95% CI [1446.64622706, 1454.61308427]; votes 54130; rank 43 | 1,471.89100% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LiveBench2026-06-25 · overall · leader | 76.79100% of row best · percent · deepseek-v4-pro · 35,014 output tokens / case | 73.2795% of row best · percent · glm-5.3-flash · 34,707 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 79.2590% of row best · points · DeepSeek V4 Pro (xhigh) · 9,652 output tokens / case | 88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark win | 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.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | DeepSeek-V4-Pro | GLM-5.3-Flash |
|---|---|---|
| Developer | DeepSeek | Z.ai |
| Family | Deepseek V4 Pro | Glm 5 3 Flash |
| Model | DeepSeek-V4-Pro | GLM-5.3-Flash |
| Version | DeepSeek-V4-Pro | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | 2026-04-24 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,000K |
| Total parameters | 1.6T | 320B |
| Active parameters | 49B | 18B |
| License | mit | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, reasoning | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
DeepSeek V4 Pro Capabilities
GLM 5.3 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Pro vs GLM 5.3 Flash FAQs
Is DeepSeek V4 Pro or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Pro or GLM 5.3 Flash?+
DeepSeek V4 Pro is $0.66 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. DeepSeek V4 Pro is $1.5441 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, DeepSeek V4 Pro or GLM 5.3 Flash?+
DeepSeek V4 Pro has the larger sourced context window. DeepSeek V4 Pro supports 1,049K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, DeepSeek V4 Pro or GLM 5.3 Flash?+
GLM 5.3 Flash leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can DeepSeek V4 Pro or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Pro is open weight; GLM 5.3 Flash is open weight.
Can DeepSeek V4 Pro and GLM 5.3 Flash understand images?+
DeepSeek V4 Pro is not 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, DeepSeek V4 Pro or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. DeepSeek V4 Pro is — and GLM 5.3 Flash is 131K.
Do DeepSeek V4 Pro and GLM 5.3 Flash support reasoning and tool use?+
DeepSeek V4 Pro: reasoning. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Pro or GLM 5.3 Flash?+
DeepSeek V4 Pro has 6 sourced provider routes; GLM 5.3 Flash has 4, so DeepSeek V4 Pro has broader tracked availability.
Which offers better value, DeepSeek V4 Pro 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.