MiniMax M2 vs GPT-4o vs Claude 3.5: how to compare them
Quick answer
MiniMax M2, GPT-4o, and Claude 3.5 are specific language-model releases, not permanent rankings of their vendors. Choose between available models using the exact workload, model ID, deployment needs, and current provider terms. The previous numerical scores and blended token prices on this page lacked enough published evidence for a reproducible comparison and have been removed.
Magic Hour publishes this guide and is included where relevant. We compare the exact named products or model versions using current documented capabilities, access, pricing mechanics, limits, and workflow fit. This is not a controlled output-quality benchmark unless a retained same-input test is explicitly described below.
What is being compared?
MiniMax M2 is a language-model release, separate from MiniMax’s Hailuo video models. GPT-4o belongs to OpenAI’s model family, while Claude 3.5 refers to an Anthropic generation that includes distinct variants. Product names alone are insufficient for an API comparison: identify the specific model and confirm that it remains available.
Choose by the task
Task | What to evaluate | What a good result must show |
|---|---|---|
Structured extraction | Field accuracy and missing information | Valid output without invented values |
Coding | Correctness in the target environment | Working changes and relevant existing checks |
Research synthesis | Source fidelity and attribution | Claims supported by the supplied evidence |
Writing | Accuracy, clarity, and editing effort | Useful copy that answers the brief |
Tool use | Correct arguments and recovery behavior | Successful completion within authorized scope |
No model can guarantee citation integrity or correct reasoning on every input. Verify consequential claims against the source material.
How to run a useful comparison
Give each candidate the same task, source pack, output requirements, and resource limits. Preserve inputs and exact model IDs. Use a predefined rubric, review the outputs without relying on the model’s self-score, and record the corrections required.
If a task needs images, audio, long context, or tools, verify that the chosen model endpoint supports them. A capability elsewhere in the vendor’s app does not automatically exist in that API model.
Compare cost without hiding the units
Use current input, output, caching, and other applicable rates for the exact endpoint. Count tool charges and retries where relevant. A single dollars-per-thousand-tokens number can be misleading when input and output prices differ.
Consult OpenAI’s API documentation, Anthropic’s documentation, and MiniMax’s platform documentation for the current models and billing. This version-specific article should not be used as a current-market leaderboard.
What should you choose for media production?
A language model can help write briefs or control an application, but it is not interchangeable with an image or video generator. For generated visual assets, pair an appropriate writing workflow with a documented media tool such as Magic Hour, then inspect the finished image or video.
Why are the old scores gone?
A score such as 9.3 out of 10 is not meaningful without the test set, weighting, model configuration, date, and evaluation process. Removing unsupported precision makes the comparison more useful than presenting an unexplained ranking as measured performance.
Frequently asked questions
MiniMax M2 is a language-model release, separate from MiniMax’s Hailuo video models. GPT-4o belongs to OpenAI’s model family, while Claude 3.5 refers to an Anthropic generation that includes distinct variants. Product names alone are insufficient for an API comparison: identify the specific model and confirm that it remains available.
A language model can help write briefs or control an application, but it is not interchangeable with an image or video generator. For generated visual assets, pair an appropriate writing workflow with a documented media tool such as Magic Hour, then inspect the finished image or video.
A score such as 9.3 out of 10 is not meaningful without the test set, weighting, model configuration, date, and evaluation process. Removing unsupported precision makes the comparison more useful than presenting an unexplained ranking as measured performance.
