MiniMax-M1 vs GPT-4o vs Claude 3 Opus vs LLaMA 3 Benchmarks

MiniMax-M1 is a new open-weight large language model (456 B parameters, ~46 B active) built with hybrid mixture-of-experts and a “lightning attention” mechanism. It natively supports up to 1 million token contexts. MiniMax-AI trained M1 for complex reasoning (math, logic, coding, long-context tasks) via reinforcement learning. In this analysis we report MiniMax-M1’s scores on key benchmarks (MMLU, GSM8K, HellaSwag, ARC, HumanEval, BBH, DROP) and compare against OpenAI’s GPT-4/GPT-4o, Anthropic’s Claude 3 Opus, and Meta’s LLaMA 3 (70B).

MMLU (Massive Multitask Language Understanding)

MMLU measures general knowledge across 57 academic and professional subjects (multiple-choice accuracy). MiniMax-M1-80K scored 81.1% on MMLU-Pro (an extended version of MMLU). This is below the top-tier models: GPT-4 achieves roughly 85–86%, Claude 3 Opus around 85% (noting evaluation differences), and LLaMA 3 (70B) is reported near 86% on standard MMLU. The table below compares MiniMax-M1 and peers:

Model	MMLU Accuracy
MiniMax-M1-80K	81.1 %
GPT-4 / GPT-4o	≈86 %
Claude 3 Opus	≈85 %
LLaMA 3 (70B)	≈86 %

Observations: MiniMax-M1’s MMLU score is solid but modestly below the latest state-of-the-art. GPT-4 (86.4%) and Claude 3 Opus lead with mid-80s accuracy, reflecting their broad knowledge. The gap (~5 points) suggests MiniMax-M1 is competitive but not yet matching the very best models on broad knowledge tasks. In practice, this means MiniMax can handle standard academic questions reasonably well, but GPT-4/Claude retain an edge in diverse subjects.

GSM8K (Grade-School Math)

GSM8K is a dataset of 8,500 grade-school math word problems requiring multi-step arithmetic. It tests chain-of-thought reasoning. MiniMax-M1 has no official published score on GSM8K, so we note peer results for context. GPT-4 scores about 92% accuracy on GSM8K (via few-shot CoT prompting). Anthropic Claude 3 Opus reaches about 95% (zero-shot), making it state-of-the-art. Meta LLaMA 3 results are not widely reported, but prior LLaMA-2 (70B) was ~57%. Our comparison:

Model	GSM8K Accuracy
MiniMax-M1-80K	N/A (unreported)
GPT-4 / GPT-4o	92 %
Claude 3 Opus	95 %
LLaMA 3 (70B)	–

Observations: GSM8K is a challenging math benchmark. GPT-4 and Claude 3 achieve very high accuracy (above 90%) with chain-of-thought prompting. Without MiniMax-M1 results, we can only note that if MiniMax matched Claude’s 95%, it would set a new bar; otherwise, it likely trails slightly. MiniMax’s emphasis on long-range context may mean it was not primarily tuned for arithmetic benchmarks.

HellaSwag (Commonsense Reasoning)

HellaSwag involves choosing the most sensible sentence completion in everyday scenarios (commonsense). Top models have essentially saturated this benchmark. GPT-4 achieves about 95% accuracy (10-shot) on HellaSwag. Claude 3 Opus scores 95.4%. No MiniMax-M1 number is available, but it likely would be similar to other high-end models if evaluated. For comparison:

Model	HellaSwag Accuracy
MiniMax-M1-80K	N/A
GPT-4 / GPT-4o	≈95 %
Claude 3 Opus	95.4 %
LLaMA 3 (70B)	–

Observations: On HellaSwag (commonsense plausibility), all top models score in the mid-90s. Claude 3’s 95.4% and GPT-4’s ~95% indicate near-human performance. We have no MiniMax data, but if it were released, we’d expect it to be in a similar range given its architecture. In practice, HellaSwag is mostly solved by frontier models, so MiniMax-M1 would need highly optimized prompting to make a difference.

ARC (AI2 Reasoning Challenge)

ARC Challenge consists of hard elementary science questions (multiple-choice). GPT-4 reportedly achieves ~96% on ARC-Challenge with few-shot chain-of-thought, dramatically higher than earlier models. (By contrast, older GPT-4 variants scored ~21% on the public leaderboard without CoT.) MiniMax-M1’s ARC performance is not published. Claude 3 Opus’s ARC score is not public. For illustration:

Model	ARC-Challenge Accuracy
MiniMax-M1-80K	N/A
GPT-4 / GPT-4o	~96 %
Claude 3 Opus	–
LLaMA 3 (70B)	–

Observations: ARC tests commonsense scientific reasoning under time constraints. GPT-4’s ~96% (few-shot) means it answers almost all questions correctly with a chain-of-thought strategy. Without MiniMax or Claude ARC numbers, we simply note that GPT-4 leads by a wide margin. If evaluated, MiniMax-M1 might rely on its extended reasoning, but its relative rank is unknown. In practice, performance on ARC is now extremely high for best models, making it less discriminative for ranking them.

HumanEval (Code Generation)

HumanEval measures code-generation (Python) correctness by pass@1. MiniMax-M1’s coding score is not reported. GPT-4 scores around 88–91% on HumanEval (depending on evaluation setting). Anthropic Claude 3 Opus scores 84.9%. We compare as follows:

Model	HumanEval Pass@1
MiniMax-M1-80K	N/A
GPT-4 / GPT-4o	≈90 %
Claude 3 Opus	84.9 %
LLaMA 3 (70B)	–

Observations: GPT-4 excels at code tasks, achieving near 90% pass rate. Claude 3’s 84.9% is strong but lower. MiniMax-M1 is aimed at reasoning, and no official HumanEval result is given; its performance on coding likely lags these specialized models. For practitioners, GPT-4/Claude remain better for code generation, while MiniMax’s advantages lie elsewhere (e.g. long-context reasoning).

BBH (BIG-Bench Hard)

BBH is a suite of 23 very difficult tasks from BIG-Bench. MiniMax-M1’s BBH score is not available. Claude 3 Opus scores 86.8% on BBH. GPT-4 (latest) also performs in the mid-80s on BBH. For comparison:

Model	BBH Accuracy
MiniMax-M1-80K	N/A
GPT-4 / GPT-4o	≈80 %
Claude 3 Opus	86.8 %
LLaMA 3 (70B)	–

Observations: BBH aggregates the hardest tasks; Claude 3’s 86.8% is state-of-the-art. GPT-4’s score (depending on version) is reported to be slightly lower. Without MiniMax data, we note that GPT-4/Claude again dominate. MiniMax-M1 may not have been explicitly optimized for these niche tasks, so its performance is unknown. The takeaway is that BBH is currently solved mostly by large-scale frontier models.

DROP (Discrete Reasoning Over Paragraphs)

DROP tests numerical reasoning on passages (F1 score). Claude 3 Opus achieves an F1 of 93.1. GPT-4 likewise scores in the mid-90s on DROP. MiniMax-M1’s DROP score has not been published. Comparison:

Model	DROP F1
MiniMax-M1-80K	N/A
GPT-4 / GPT-4o	≈95
Claude 3 Opus	93.1
LLaMA 3 (70B)	–

Observations: Both GPT-4 and Claude 3 handle DROP nearly perfectly, reflecting strong reading and arithmetic skills. Claude’s 93.1 F1 is top-tier. MiniMax-M1’s focus is broad reasoning, and no DROP result is given. In practice, MiniMax’s strengths in long-context reasoning may not directly impact DROP performance (which is short passages).

Summary: MiniMax-M1 shows very strong performance on many benchmarks, especially those involving long context and complex reasoning (as noted in its tech report). However, on standard leaderboards like MMLU or GSM8K, it trails slightly behind GPT-4/GPT-4o and Claude 3 Opus. The tables above illustrate that GPT-4 and Claude generally lead in accuracy. Key insights:

• MiniMax-M1’s MMLU score (~81.1%) is good but several points below GPT-4/Claude.
• On math (GSM8K), GPT-4 and Claude achieve 92–95%; MiniMax-M1’s result is unreported but unlikely to dramatically exceed these.
• In commonsense tasks (HellaSwag, ARC), GPT-4/Claude are at human-level performance; MiniMax-M1 presumably would be similar if evaluated.
• For coding (HumanEval) and hardest tasks (BBH), GPT-4/Claude again lead; MiniMax-M1 has no published numbers, suggesting it was optimized elsewhere.

Overall, MiniMax-M1 is a competitive open model, but GPT-4 and Claude 3 Opus remain the strongest performers on these standard benchmarks. These comparisons help ML practitioners understand MiniMax’s relative strengths: it excels at long-context and hybrid-attention tasks, while GPT-4/Claude retain an edge on many conventional academic and reasoning exams.