Model Comparison

North Mini Code 1.0 vs DeepSeek-V4-Pro-MaxWhich is better in 2026?

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks.

Verdict: North Mini Code 1.0 vs DeepSeek-V4-Pro-Max — which is better?

North Mini Code 1.0 (by Cohere) and DeepSeek-V4-Pro-Max (by DeepSeek) 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.

North Mini Code 1.0 outperforms in 0 benchmarks, while DeepSeek-V4-Pro-Max is better at 3 benchmarks (SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0). DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks.

Choose North Mini Code 1.0 if…

  • you want the most recent training data — it shipped Jun 2026

Choose DeepSeek-V4-Pro-Max if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

North Mini Code 1.0 outperforms in 0 benchmarks, while DeepSeek-V4-Pro-Max is better at 3 benchmarks (SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0).

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

1570.0B diff

DeepSeek-V4-Pro-Max has 1570.0B more parameters than North Mini Code 1.0, making it 5233.3% larger.

Cohere
North Mini Code 1.0
30.0Bparameters
DeepSeek
DeepSeek-V4-Pro-Max
1.6Tparameters
30.0B
North Mini Code 1.0
1600.0B
DeepSeek-V4-Pro-Max

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-Max specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-Max specifies output context (131,072 tokens).

Cohere
North Mini Code 1.0
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V4-Pro-Max
Input1,048,576 tokens
Output131,072 tokens
Fri Jul 17 2026 • llm-stats.com

License

Usage and distribution terms

North Mini Code 1.0 is licensed under Apache 2.0, while DeepSeek-V4-Pro-Max uses MIT.

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

North Mini Code 1.0

Apache 2.0

Open weights

DeepSeek-V4-Pro-Max

MIT

Open weights

Release Timeline

When each model was launched

North Mini Code 1.0 was released on 2026-06-09, while DeepSeek-V4-Pro-Max was released on 2026-04-23.

North Mini Code 1.0 is 2 months newer than DeepSeek-V4-Pro-Max.

North Mini Code 1.0

Jun 9, 2026

1 months ago

1mo newer
DeepSeek-V4-Pro-Max

Apr 23, 2026

2 months ago

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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Cohere

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

DeepSeek
Larger context window (1,048,576 tokens)
Higher SWE-Bench Pro score (55.4% vs 40.2%)
Higher SWE-Bench Verified score (80.6% vs 67.6%)
Higher Terminal-Bench 2.0 score (67.9% vs 36.0%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against North Mini Code 1.0 and DeepSeek-V4-Pro-Max side-by-side, then vote on the output you prefer.

North Mini Code 1.0
✓ Preferred
DeepSeek-V4-Pro-Max
Open in Playground
AI Model Comparison Table
Feature
Cohere
North Mini Code 1.0
DeepSeek
DeepSeek-V4-Pro-Max

FAQ

Common questions about North Mini Code 1.0 vs DeepSeek-V4-Pro-Max.

Which is better, North Mini Code 1.0 or DeepSeek-V4-Pro-Max?

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks. North Mini Code 1.0 is made by Cohere and DeepSeek-V4-Pro-Max is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does North Mini Code 1.0 compare to DeepSeek-V4-Pro-Max in benchmarks?

North Mini Code 1.0 scores LiveCodeBench v6: 70.3%, SWE-Bench Verified: 67.6%, SWE-Bench Pro: 40.2%, SciCode: 38.2%, Terminal-Bench 2.0: 36.0%. DeepSeek-V4-Pro-Max scores CodeForces: 100.0%, HMMT Feb 26: 95.2%, LiveCodeBench: 93.5%, MathArena Apex: 90.2%, GPQA: 90.1%.

What are the context window sizes for North Mini Code 1.0 and DeepSeek-V4-Pro-Max?

North Mini Code 1.0 supports an unknown number of tokens and DeepSeek-V4-Pro-Max 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 North Mini Code 1.0 and DeepSeek-V4-Pro-Max?

Key differences include licensing (Apache 2.0 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes North Mini Code 1.0 and DeepSeek-V4-Pro-Max?

North Mini Code 1.0 is developed by Cohere and DeepSeek-V4-Pro-Max is developed by DeepSeek.

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