Model Comparison

MiniMax M2.7 vs GLM-5.1Which is better in 2026?

GLM-5.1 significantly outperforms across most benchmarks. MiniMax M2.7 is 4.1x cheaper per token.

Verdict: MiniMax M2.7 vs GLM-5.1 — which is better?

MiniMax M2.7 (by MiniMax) and GLM-5.1 (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.

MiniMax M2.7 outperforms in 1 benchmarks (Toolathlon), while GLM-5.1 is better at 5 benchmarks (Finance Agent v2, GDPval-AA, NL2Repo, SWE-Bench Pro, Terminal-Bench 2.0). GLM-5.1 significantly outperforms across most benchmarks.

On price, MiniMax M2.7 is roughly 4.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5.1 also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose MiniMax M2.7 if…

  • cost matters — it's about 4.1x cheaper per token

Choose GLM-5.1 if…

  • you want the strongest raw capability — it leads on 5 of 6 shared benchmarks
  • you process long inputs — it offers a 200,000 token context window
  • you want the most recent training data — it shipped Apr 2026

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

MiniMax M2.7 outperforms in 1 benchmarks (Toolathlon), while GLM-5.1 is better at 5 benchmarks (Finance Agent v2, GDPval-AA, NL2Repo, SWE-Bench Pro, Terminal-Bench 2.0).

GLM-5.1 significantly outperforms across most benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

MiniMax M2.7 costs less

For input processing, MiniMax M2.7 ($0.30/1M tokens) is 4.7x cheaper than GLM-5.1 ($1.40/1M tokens).

For output processing, MiniMax M2.7 ($1.20/1M tokens) is 3.7x cheaper than GLM-5.1 ($4.40/1M tokens).

In conclusion, GLM-5.1 is more expensive than MiniMax M2.7.*

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

Lowest available price from all providers
Fri Jul 17 2026 • llm-stats.com
MiniMax
MiniMax M2.7
Input tokens$0.30
Output tokens$1.20
Best providerFireworks
Zhipu AI
GLM-5.1
Input tokens$1.40
Output tokens$4.40
Best providerFriendliAI
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

GLM-5.1 accepts 200,000 input tokens compared to MiniMax M2.7's 196,608 tokens. MiniMax M2.7 can generate longer responses up to 196,608 tokens, while GLM-5.1 is limited to 128,000 tokens.

MiniMax
MiniMax M2.7
Input196,608 tokens
Output196,608 tokens
Zhipu AI
GLM-5.1
Input200,000 tokens
Output128,000 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.

MiniMax M2.7

MIT

Open weights

GLM-5.1

MIT

Open weights

Release Timeline

When each model was launched

MiniMax M2.7 was released on 2026-03-18, while GLM-5.1 was released on 2026-04-07.

GLM-5.1 is 1 month newer than MiniMax M2.7.

MiniMax M2.7

Mar 18, 2026

4 months ago

GLM-5.1

Apr 7, 2026

3 months ago

2w 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

MiniMax M2.7 is available from Fireworks, MiniMax, Novita. GLM-5.1 is available from FriendliAI, ZAI.

MiniMax M2.7

fireworks logo
Fireworks
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
minimax logo
MiniMax
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

GLM-5.1

friendli logo
FriendliAI
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

MiniMax
Less expensive input tokens
Less expensive output tokens
Higher Toolathlon score (46.3% vs 40.7%)
Zhipu AI
Larger context window (200,000 tokens)
Higher Finance Agent v2 score (44.8% vs 27.9%)
Higher GDPval-AA score (42.7% vs 39.3%)
Higher NL2Repo score (42.7% vs 39.8%)
Higher SWE-Bench Pro score (58.4% vs 56.2%)
Higher Terminal-Bench 2.0 score (69.0% vs 57.0%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against MiniMax M2.7 and GLM-5.1 side-by-side, then vote on the output you prefer.

MiniMax M2.7
✓ Preferred
GLM-5.1
Open in Playground
AI Model Comparison Table
Feature
MiniMax
MiniMax M2.7
Zhipu AI
GLM-5.1

FAQ

Common questions about MiniMax M2.7 vs GLM-5.1.

Which is better, MiniMax M2.7 or GLM-5.1?

GLM-5.1 significantly outperforms across most benchmarks. MiniMax M2.7 is made by MiniMax and GLM-5.1 is made by Zhipu AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does MiniMax M2.7 compare to GLM-5.1 in benchmarks?

MiniMax M2.7 scores SWE-bench Multilingual: 76.5%, MLE-Bench Lite: 66.6%, MM-ClawBench: 62.7%, Terminal-Bench 2.0: 57.0%, SWE-Bench Pro: 56.2%. GLM-5.1 scores Vending-Bench 2: 100.0%, AIME 2026: 95.3%, HMMT 2025: 94.0%, GPQA: 86.2%, IMO-AnswerBench: 83.8%.

Is MiniMax M2.7 cheaper than GLM-5.1?

MiniMax M2.7 is 4.7x cheaper for input tokens. MiniMax M2.7 costs $0.30/M input and $1.20/M output via fireworks. GLM-5.1 costs $1.40/M input and $4.40/M output via friendli.

What are the context window sizes for MiniMax M2.7 and GLM-5.1?

MiniMax M2.7 supports 197K tokens and GLM-5.1 supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between MiniMax M2.7 and GLM-5.1?

Key differences include context window (197K vs 200K), input pricing ($0.30 vs $1.40/M). See the full comparison above for benchmark-by-benchmark results.

Who makes MiniMax M2.7 and GLM-5.1?

MiniMax M2.7 is developed by MiniMax and GLM-5.1 is developed by Zhipu AI.

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