{"id":4029,"date":"2025-08-31T11:59:00","date_gmt":"2025-08-31T06:59:00","guid":{"rendered":"https:\/\/www.edopedia.com\/blog\/?p=4029"},"modified":"2025-10-21T06:30:33","modified_gmt":"2025-10-21T01:30:33","slug":"minimax-m1-vs-gpt-4o-vs-claude-3-opus-vs-llama-3-benchmarks","status":"publish","type":"post","link":"https:\/\/www.edopedia.com\/blog\/minimax-m1-vs-gpt-4o-vs-claude-3-opus-vs-llama-3-benchmarks\/","title":{"rendered":"MiniMax-M1 vs GPT-4o vs Claude 3 Opus vs LLaMA\u00a03 Benchmarks"},"content":{"rendered":"\n

MiniMax-M1 is a new open-weight large language model (456\u202fB parameters, ~46\u202fB active) built with hybrid mixture-of-experts and a \u201clightning attention\u201d mechanism. It natively supports up to 1\u202fmillion token<\/strong> contexts. MiniMax-AI trained M1 for complex reasoning (math, logic, coding, long-context tasks) via reinforcement learning. In this analysis we report MiniMax-M1\u2019s scores on key benchmarks (MMLU, GSM8K, HellaSwag, ARC, HumanEval, BBH, DROP) and compare against OpenAI\u2019s GPT-4\/GPT-4o, Anthropic\u2019s Claude 3 Opus, and Meta\u2019s LLaMA 3 (70B).<\/p>\n\n\n\n

MMLU (Massive Multitask Language Understanding)<\/h2>\n\n\n\n

MMLU measures general knowledge across 57 academic and professional subjects (multiple-choice accuracy). MiniMax-M1-80K scored 81.1%<\/strong> on MMLU-Pro (an extended version of MMLU). This is below the top-tier models: GPT-4 achieves roughly 85\u201386%<\/strong>, Claude 3 Opus around 85%<\/strong> (noting evaluation differences), and LLaMA 3 (70B) is reported near 86%<\/strong> on standard MMLU. The table below compares MiniMax-M1 and peers:<\/p>\n\n\n\n

Model<\/th>MMLU Accuracy<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>81.1\u202f%<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>\u224886\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>\u224885\u202f%<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u224886\u202f%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> MiniMax-M1\u2019s 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.<\/p>\n\n\n\n

GSM8K (Grade-School Math)<\/h2>\n\n\n\n

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<\/strong> on GSM8K, so we note peer results for context. GPT-4 scores about 92%<\/strong> accuracy on GSM8K (via few-shot CoT prompting). Anthropic Claude 3 Opus reaches about 95%<\/strong> (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:<\/p>\n\n\n\n

Model<\/th>GSM8K Accuracy<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A (unreported)<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>92\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>95\u202f%<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> 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\u2019s 95%, it would set a new bar; otherwise, it likely trails slightly. MiniMax\u2019s emphasis on long-range context may mean it was not primarily tuned for arithmetic benchmarks.<\/p>\n\n\n\n

HellaSwag (Commonsense Reasoning)<\/h2>\n\n\n\n

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

Model<\/th>HellaSwag Accuracy<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>\u224895\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>95.4\u202f%<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> On HellaSwag (commonsense plausibility), all top models score in the mid-90s. Claude 3\u2019s 95.4% and GPT-4\u2019s ~95% indicate near-human performance. We have no MiniMax data, but if it were released, we\u2019d 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.<\/p>\n\n\n\n

ARC (AI2 Reasoning Challenge)<\/h2>\n\n\n\n

ARC Challenge consists of hard elementary science questions (multiple-choice). GPT-4 reportedly achieves ~96%<\/strong> 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\u2019s ARC performance is not published. Claude 3 Opus\u2019s ARC score is not public. For illustration:<\/p>\n\n\n\n

Model<\/th>ARC-Challenge Accuracy<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>~96\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>\u2013<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> ARC tests commonsense scientific reasoning under time constraints. GPT-4\u2019s ~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.<\/p>\n\n\n\n

HumanEval (Code Generation)<\/h2>\n\n\n\n

HumanEval measures code-generation (Python) correctness by pass@1. MiniMax-M1\u2019s coding score is not reported. GPT-4 scores around 88\u201391%<\/strong> on HumanEval (depending on evaluation setting). Anthropic Claude 3 Opus scores 84.9%<\/strong>. We compare as follows:<\/p>\n\n\n\n

Model<\/th>HumanEval Pass@1<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>\u224890\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>84.9\u202f%<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> GPT-4 excels at code tasks, achieving near 90% pass rate. Claude 3\u2019s 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\u2019s advantages lie elsewhere (e.g. long-context reasoning).<\/p>\n\n\n\n

BBH (BIG-Bench Hard)<\/h2>\n\n\n\n

BBH is a suite of 23 very difficult tasks from BIG-Bench. MiniMax-M1\u2019s BBH score is not available. Claude 3 Opus scores 86.8%<\/strong> on BBH. GPT-4 (latest) also performs in the mid-80s on BBH. For comparison:<\/p>\n\n\n\n

Model<\/th>BBH Accuracy<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>\u224880\u202f%<\/td><\/tr>
Claude 3 Opus<\/td>86.8\u202f%<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> BBH aggregates the hardest tasks; Claude 3\u2019s 86.8% is state-of-the-art. GPT-4\u2019s 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.<\/p>\n\n\n\n

DROP (Discrete Reasoning Over Paragraphs)<\/h2>\n\n\n\n

DROP tests numerical reasoning on passages (F1 score). Claude 3 Opus achieves an F1 of 93.1<\/strong>. GPT-4 likewise scores in the mid-90s on DROP. MiniMax-M1\u2019s DROP score has not been published. Comparison:<\/p>\n\n\n\n

Model<\/th>DROP F1<\/th><\/tr><\/thead>
MiniMax-M1-80K<\/td>N\/A<\/td><\/tr>
GPT-4 \/ GPT-4o<\/td>\u224895<\/td><\/tr>
Claude 3 Opus<\/td>93.1<\/td><\/tr>
LLaMA 3 (70B)<\/td>\u2013<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Observations:<\/strong> Both GPT-4 and Claude 3 handle DROP nearly perfectly, reflecting strong reading and arithmetic skills. Claude\u2019s 93.1 F1 is top-tier. MiniMax-M1\u2019s focus is broad reasoning, and no DROP result is given. In practice, MiniMax\u2019s strengths in long-context reasoning may not directly impact DROP performance (which is short passages).<\/p>\n\n\n\n

Summary:<\/strong> 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:<\/p>\n\n\n\n