As of August 20, 2026, MiniMax M3 is 88% cheaper per million tokens than Command A+. Command A+ is $2.50 / $10.00 per million input/output tokens versus MiniMax M3 at $0.30 / $1.20. Source: https://anotherwrapper.com/tools/llm-pricing
Source: AnotherWrapper LLM pricing (https://anotherwrapper.com/tools/llm-pricing)
Command A+
Cohere
Input
$2.50
Output
$10.00
MiniMax M3
MiniMax
Input
$0.30
Output
$1.20
Token pricing, specs, and benchmarks side by side
Command A+MiniMax M3Verdict
MiniMax M3 offers both lower pricing and stronger benchmark performance across the board, making it the clear value leader in this comparison.
As of August 20, 2026, MiniMax M3 is 88% cheaper on blended LLM API pricing. MiniMax M3 costs $0.30 per million input tokens and $1.20 per million output tokens ($1.50 blended 1M-in + 1M-out). Command A+ costs $2.50 / $10.00 per million tokens ($12.50 blended). Sources: https://anotherwrapper.com/tools/llm-pricing and https://anotherwrapper.com/tools/llm-pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
Command A+ Cohere API pricing is $2.50 input and $10.00 output per million tokens via Cohere. MiniMax M3 MiniMax API pricing is $0.30 input and $1.20 output per million tokens via MiniMax. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
MiniMax M3 has a published SWE-bench Verified score of 80.5%. Command A+ does not have that eval on this page yet.
Command A+ supports a token context window, while MiniMax M3 supports 1M tokens. MiniMax M3 offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
MiniMax M3 leads on 4 of 5 shared benchmarks versus Command A+'s 1 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both Command A+ and MiniMax M3 are production API models. For cost-sensitive production traffic, MiniMax M3 has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call Command A+ through Cohere and MiniMax M3 through MiniMax with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published Cohere API pricing and MiniMax API pricing, quoted per million tokens. Official rate sheets: https://anotherwrapper.com/tools/llm-pricing and https://anotherwrapper.com/tools/llm-pricing. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
Command A+ supports up to an unspecified number of output tokens per request, while MiniMax M3 supports up to 131K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Neither Command A+ nor MiniMax M3 has published throughput data on this page yet. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Command A+ or MiniMax M3 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. Command A+ costs $2.50/M input and $10.00/M output. MiniMax M3 costs $0.30/M input and $1.20/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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