Model Comparison

MiMo-V2.5-Pro vs GLM-5.2Which is better in 2026?

GLM-5.2 significantly outperforms across most benchmarks. MiMo-V2.5-Pro is 2.7x cheaper per token.

Verdict: MiMo-V2.5-Pro vs GLM-5.2 — which is better?

MiMo-V2.5-Pro (by Xiaomi) and GLM-5.2 (by Zhipu AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

MiMo-V2.5-Pro outperforms in 0 benchmarks, while GLM-5.2 is better at 3 benchmarks (GPQA, Humanity's Last Exam, SWE-Bench Pro). GLM-5.2 significantly outperforms across most benchmarks.

On price, MiMo-V2.5-Pro is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Choose MiMo-V2.5-Pro if…

  • cost matters — it's about 2.7x cheaper per token

Choose GLM-5.2 if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
  • you want the most recent training data — it shipped Jun 2026

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

MiMo-V2.5-Pro outperforms in 0 benchmarks, while GLM-5.2 is better at 3 benchmarks (GPQA, Humanity's Last Exam, SWE-Bench Pro).

GLM-5.2 significantly outperforms across most benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

MiMo-V2.5-Pro costs less

For input processing, MiMo-V2.5-Pro ($0.43/1M tokens) is 2.2x cheaper than GLM-5.2 ($0.95/1M tokens).

For output processing, MiMo-V2.5-Pro ($0.87/1M tokens) is 3.4x cheaper than GLM-5.2 ($3.00/1M tokens).

In conclusion, GLM-5.2 is more expensive than MiMo-V2.5-Pro.*

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

Lowest available price from all providers
Fri Jul 17 2026 • llm-stats.com
Xiaomi
MiMo-V2.5-Pro
Input tokens$0.43
Output tokens$0.87
Best providerXiaomi
Zhipu AI
GLM-5.2
Input tokens$0.95
Output tokens$3.00
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

270.2B diff

MiMo-V2.5-Pro has 270.2B more parameters than GLM-5.2, making it 35.9% larger.

Xiaomi
MiMo-V2.5-Pro
1.0Tparameters
Zhipu AI
GLM-5.2
753.0Bparameters
1023.2B
MiMo-V2.5-Pro
753.0B
GLM-5.2

Context Window

Maximum input and output token capacity

Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 131,072 tokens.

Xiaomi
MiMo-V2.5-Pro
Input1,048,576 tokens
Output131,072 tokens
Zhipu AI
GLM-5.2
Input1,048,576 tokens
Output131,072 tokens
Fri Jul 17 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

MiMo-V2.5-Pro

MIT

Open weights

GLM-5.2

MIT

Open weights

Release Timeline

When each model was launched

MiMo-V2.5-Pro was released on 2026-04-27, while GLM-5.2 was released on 2026-06-16.

GLM-5.2 is 2 months newer than MiMo-V2.5-Pro.

MiMo-V2.5-Pro

Apr 27, 2026

2 months ago

GLM-5.2

Jun 16, 2026

1 months ago

1mo 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

MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita. GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI.

MiMo-V2.5-Pro

xiaomi logo
Xiaomi
Input Price:Input: $0.43/1MOutput Price:Output: $0.87/1M
deepinfra logo
Deepinfra
Input Price:Input: $1.00/1MOutput Price:Output: $3.00/1M
novita logo
Novita
Input Price:Input: $2.00/1MOutput Price:Output: $6.00/1M

GLM-5.2

deepinfra logo
Deepinfra
Input Price:Input: $0.95/1MOutput Price:Output: $3.00/1M
fireworks logo
Fireworks
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
friendli logo
FriendliAI
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
novita logo
Novita
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
together logo
Together
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
z logo
Unknown Organization
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Less expensive input tokens
Less expensive output tokens
Higher GPQA score (91.2% vs 66.7%)
Higher Humanity's Last Exam score (54.7% vs 34.0%)
Higher SWE-Bench Pro score (62.1% vs 57.2%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against MiMo-V2.5-Pro and GLM-5.2 side-by-side, then vote on the output you prefer.

MiMo-V2.5-Pro
✓ Preferred
GLM-5.2
Open in Playground
AI Model Comparison Table
Feature
Xiaomi
MiMo-V2.5-Pro
Zhipu AI
GLM-5.2

FAQ

Common questions about MiMo-V2.5-Pro vs GLM-5.2.

Which is better, MiMo-V2.5-Pro or GLM-5.2?

GLM-5.2 significantly outperforms across most benchmarks. MiMo-V2.5-Pro is made by Xiaomi and GLM-5.2 is made by Zhipu AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does MiMo-V2.5-Pro compare to GLM-5.2 in benchmarks?

MiMo-V2.5-Pro scores FrontierSWE (Impl.): 100.0%, GSM8k: 99.6%, ARC-C: 97.2%, MMLU-Redux: 92.8%, C-Eval: 91.5%. GLM-5.2 scores AIME 2026: 99.2%, HMMT 2025: 94.4%, HMMT Feb 26: 92.5%, GPQA: 91.2%, IMO-AnswerBench: 91.0%.

Is MiMo-V2.5-Pro cheaper than GLM-5.2?

MiMo-V2.5-Pro is 2.2x cheaper for input tokens. MiMo-V2.5-Pro costs $0.43/M input and $0.87/M output via xiaomi. GLM-5.2 costs $0.95/M input and $3.00/M output via deepinfra.

What are the context window sizes for MiMo-V2.5-Pro and GLM-5.2?

MiMo-V2.5-Pro supports 1.0M tokens and GLM-5.2 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 MiMo-V2.5-Pro and GLM-5.2?

Key differences include input pricing ($0.43 vs $0.95/M). See the full comparison above for benchmark-by-benchmark results.

Who makes MiMo-V2.5-Pro and GLM-5.2?

MiMo-V2.5-Pro is developed by Xiaomi and GLM-5.2 is developed by Zhipu AI.