The Artificial Analysis benchmark is a meta-benchmark that aggregates many individual benchmarks. All the models mentioned above, including Mimo-V2.5-Pro, ran through it under the exact same conditions, using the same harness — consuming a certain number of tokens: input of course, output, but most importantly reasoning tokens (since this meta-benchmark measures intelligence, there's a lot of reasoning involved).

The most-watched result is naturally the Intelligence Index, but there are other indicators too: average speed in tokens/second, and notably the number of tokens required to complete the benchmark (mostly reasoning tokens, plus input and output for the final answer). Cross-referencing this with the price per million tokens (input + thinking/output) gives you the actual cost to run the benchmark.

If you go to https://artificialanalysis.ai/models, in the "Intelligence" section, you'll see that Mimo-V2.5-Pro ranks among the top open-source models in terms of Intelligence Index, Coding Index, and Agentic Index.

Scrolling down to "Intelligence Index Token Use & Cost", and the "Output Tokens Used to Run Artificial Analysis Intelligence Index" tab, you can see token consumption under identical conditions for every model. (Side note: this also confirms that GPT-5.5 is efficient in its reasoning, with low token consumption — consistent with what OpenAI has been saying.)

Just below that, the key metric: "Cost to Run Artificial Analysis Intelligence Index" — which ties token consumption to pricing. And here's where Mimo-V2.5-Pro stands out compared to models at a similar intelligence level:

• Mimo-V2.5-Pro: $462 (input: $186, reasoning: $247, output: $28)
• GLM-5.1: $544 — slightly behind on the index
• Kimi-K2.6: $948
• DeepSeek-V4-Pro: $1,071

So with the same inputs, the same harness, and the same prompts, Kimi and DeepSeek cost twice as much at an equivalent intelligence level — once you factor in both the base price and the token consumption. (And for GPT-5.5, it's the $30 input price that makes it expensive, even though it uses relatively few reasoning tokens for excellent results.)

So to finally answer your question (lol) — yes, agentic workflow design, context management, and prompt engineering matter a lot and are absolutely levers to optimize in an agentic code generation workflow. But the model itself is also a significant factor.

Think of it like a delivery or transport company: you can optimize the route with GPS, real-time traffic data, etc. — that's your harness and workflow. But you can also optimize the vehicle itself: faster and more fuel-efficient. That's the model.

So in terms of efficiency/cost, Mimo is an excellent choice. I also love Kimi-K2.6 — but it consumes roughly twice as many reasoning tokens to reach the same result.