Model Comparison

DeepSeek-R1 vs MiniMax M2.5Which is better in 2026?

Comparing DeepSeek-R1 and MiniMax M2.5 across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-R1 vs MiniMax M2.5 — which is better?

DeepSeek-R1 (by DeepSeek) and MiniMax M2.5 (by MiniMax) 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.

On price, MiniMax M2.5 is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

MiniMax M2.5 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek-R1 if…

  • you want predictable pricing at $0.55/M input and $2.19/M output

Choose MiniMax M2.5 if…

  • cost matters — it's about 1.8x 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 Feb 2026
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and MiniMax M2.5 don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

MiniMax M2.5 costs less

For input processing, DeepSeek-R1 ($0.55/1M tokens) is 1.8x more expensive than MiniMax M2.5 ($0.30/1M tokens).

For output processing, DeepSeek-R1 ($2.19/1M tokens) is 1.8x more expensive than MiniMax M2.5 ($1.20/1M tokens).

In conclusion, DeepSeek-R1 is more expensive than MiniMax M2.5.*

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

Lowest available price from all providers
Sun Jun 07 2026 • llm-stats.com
DeepSeek
DeepSeek-R1
Input tokens$0.55
Output tokens$2.19
Best providerDeepSeek
MiniMax
MiniMax M2.5
Input tokens$0.30
Output tokens$1.20
Best providerMiniMax
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

441.0B diff

DeepSeek-R1 has 441.0B more parameters than MiniMax M2.5, making it 191.7% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
MiniMax
MiniMax M2.5
230.0Bparameters
671.0B
DeepSeek-R1
230.0B
MiniMax M2.5

Context Window

Maximum input and output token capacity

MiniMax M2.5 accepts 1,000,000 input tokens compared to DeepSeek-R1's 131,072 tokens. MiniMax M2.5 can generate longer responses up to 1,000,000 tokens, while DeepSeek-R1 is limited to 131,072 tokens.

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
MiniMax
MiniMax M2.5
Input1,000,000 tokens
Output1,000,000 tokens
Sun Jun 07 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.

DeepSeek-R1

MIT

Open weights

MiniMax M2.5

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while MiniMax M2.5 was released on 2026-02-12.

MiniMax M2.5 is 13 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.4 years ago

MiniMax M2.5

Feb 12, 2026

3 months ago

1.1yr 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-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. MiniMax M2.5 is available from MiniMax.

DeepSeek-R1

deepseek logo
DeepSeek
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.85/1MOutput Price:Output: $2.50/1M
together logo
Together
Input Price:Input: $7.00/1MOutput Price:Output: $7.00/1M
fireworks logo
Fireworks
Input Price:Input: $8.00/1MOutput Price:Output: $8.00/1M

MiniMax M2.5

minimax logo
MiniMax
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek

No standout differentiators in the data we have for this pair.

MiniMax
Larger context window (1,000,000 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
MiniMax
MiniMax M2.5

FAQ

Common questions about DeepSeek-R1 vs MiniMax M2.5.

Which is better, DeepSeek-R1 or MiniMax M2.5?

DeepSeek-R1 (DeepSeek) and MiniMax M2.5 (MiniMax) 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-R1 compare to MiniMax M2.5 in benchmarks?

MiniMax M2.5 scores SWE-Bench Verified: 80.2%, BFCL_v3_MultiTurn: 76.8%, BrowseComp: 76.3%, MEWC: 74.4%, GDPval-MM: 59.0%.

Is DeepSeek-R1 cheaper than MiniMax M2.5?

MiniMax M2.5 is 1.8x cheaper for input tokens. DeepSeek-R1 costs $0.55/M input and $2.19/M output via deepseek. MiniMax M2.5 costs $0.30/M input and $1.20/M output via minimax.

What are the context window sizes for DeepSeek-R1 and MiniMax M2.5?

DeepSeek-R1 supports 131K tokens and MiniMax M2.5 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-R1 and MiniMax M2.5?

Key differences include context window (131K vs 1.0M), input pricing ($0.55 vs $0.30/M). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-R1 and MiniMax M2.5?

DeepSeek-R1 is developed by DeepSeek and MiniMax M2.5 is developed by MiniMax.

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