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MiniMax M2.7 vs R1 Distill Llama 70B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for MiniMax M2.7 and R1 Distill Llama 70B.

MiniMax

MiniMax M2.7

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

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DeepSeek

R1 Distill Llama 70B

DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...

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Technical Specifications

SpecificationMiniMax M2.7R1 Distill Llama 70B
ProviderMiniMaxDeepSeek
Context Window204,800 tokens128,000 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-03-182025-01-23

API Pricing Comparison

Input Price per Million Tokens

MiniMax M2.7

$0.18

R1 Distill Llama 70B

$0.80

Output Price per Million Tokens

MiniMax M2.7

$0.72

R1 Distill Llama 70B

$0.80

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8250.0%vs8520.0%
MiniMax M2.7
R1 Distill Llama 70B
HumanEvalPython coding & logic synthesis
8000.0%vs8830.0%
MiniMax M2.7
R1 Distill Llama 70B
MATHComplex mathematical problem solving
5400.0%vs7000.0%
MiniMax M2.7
R1 Distill Llama 70B
GPQAGraduate-level expert reasoning
3900.0%vs4450.0%
MiniMax M2.7
R1 Distill Llama 70B
HellaSwagCommonsense reasoning and inference
8400.0%vs8600.0%
MiniMax M2.7
R1 Distill Llama 70B
MT-BenchMulti-turn conversation flow quality
870.0%vs905.0%
MiniMax M2.7
R1 Distill Llama 70B

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.

R1 Distill Llama 70B Quirks & Gotchas

No developer gotchas reported.