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DeepSeek-V3 vs GLM-5.3-Flash

Comparing DeepSeek-V3 and GLM-5.3-Flash across benchmarks, pricing, and capabilities.

DeepSeek · Zhipu AI · Updated for 2026

Which is better?

DeepSeek-V3 and GLM-5.3-Flash trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

On price, GLM-5.3-Flash is roughly 2.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5.3-Flash also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V3

  • you want predictable pricing at $0.27/M input and $1.10/M output

Choose GLM-5.3-Flash

  • cost matters — it's about 2.0x cheaper per token
  • you process long inputs — it offers a 1,000,000 token context window
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
GLM-5.3-Flash
Zhipu AI
Benchmark wins
—
—
Input price
$0.27 / M
$0.15 / M
Output price
$1.10 / M
$0.50 / M
Context window
131,072
1,000,000
Released
Dec 2024
Aug 2026
License
MIT + Model License (Commercial use allowed)
MIT

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3 and GLM-5.3-Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Pricing Analysis

Price comparison per million tokens

GLM-5.3-Flash costs less

For input processing, DeepSeek-V3 ($0.27/1M tokens) is 1.8x more expensive than GLM-5.3-Flash ($0.15/1M tokens).

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 2.2x more expensive than GLM-5.3-Flash ($0.50/1M tokens).

In conclusion, DeepSeek-V3 is more expensive than GLM-5.3-Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Aug 26 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Zhipu AI
GLM-5.3-Flash
Input tokens$0.15
Output tokens$0.50
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

351.0B diff

DeepSeek-V3 has 351.0B more parameters than GLM-5.3-Flash, making it 109.7% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Zhipu AI
GLM-5.3-Flash
320.0Bparameters
671.0B
DeepSeek-V3
320.0B
GLM-5.3-Flash

Context Window

Maximum input and output token capacity

GLM-5.3-Flash accepts 1,000,000 input tokens compared to DeepSeek-V3's 131,072 tokens. Both models can generate responses up to 131,072 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Zhipu AI
GLM-5.3-Flash
Input1,000,000 tokens
Output131,072 tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V3 does not.

GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3

Text
Images
Audio
Video

GLM-5.3-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while GLM-5.3-Flash uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

GLM-5.3-Flash

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while GLM-5.3-Flash was released on 2026-08-26.

GLM-5.3-Flash is 20 months newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.7 years ago

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.7yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V3 is available from DeepSeek. GLM-5.3-Flash is available from ZAI.

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

GLM-5.3-Flash

z logo
Unknown Organization
Input Price:Input: $0.15/1MOutput Price:Output: $0.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
GLM-5.3-Flash
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs GLM-5.3-Flash.

Which is better, DeepSeek-V3 or GLM-5.3-Flash?

DeepSeek-V3 (DeepSeek) and GLM-5.3-Flash (Zhipu AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3 compare to GLM-5.3-Flash in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, MVBench: 77.8%.

Is DeepSeek-V3 cheaper than GLM-5.3-Flash?

GLM-5.3-Flash is 1.8x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. GLM-5.3-Flash costs $0.15/M input and $0.50/M output via z.

What are the context window sizes for DeepSeek-V3 and GLM-5.3-Flash?

DeepSeek-V3 supports 131K tokens and GLM-5.3-Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3 and GLM-5.3-Flash?

Key differences include context window (131K vs 1.0M), input pricing ($0.27 vs $0.15/M), multimodal support (no vs yes), licensing (MIT + Model License (Commercial use allowed) vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and GLM-5.3-Flash?

DeepSeek-V3 is developed by DeepSeek and GLM-5.3-Flash is developed by Zhipu AI.

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