Analysis · 2026-09-22

Grok 4.7 vs MiMo-V2.6 Pro: which agent model offers better value?

Grok 4.7 and MiMo-V2.6 Pro reach similar independent intelligence scores through very different pricing, deployment, and agent-performance trade-offs.

Models covered: Grok 4.7, MiMo-V2.6 Pro, GPT-5.6 Sol, Claude Opus 5

The same intelligence score does not make these interchangeable models

Grok 4.7 and MiMo-V2.6 Pro arrived within a day of each other and both score 46 on Artificial Analysis Intelligence Index v4.3.2. That neat tie hides the more useful story. Grok is a proprietary hosted model aimed at coding agents and professional knowledge work. MiMo is an MIT-licensed, open-weight omnimodal model that accepts text, images, video, and audio and can be served privately. Their list prices, context windows, token consumption, and infrastructure requirements differ enough that choosing between them from the composite score alone would be a mistake.

The independent score is still important. Artificial Analysis reports Grok 4.7 at 46 after a two-point gain over Grok 4.6, while MiMo-V2.6 Pro debuts at 46 and ranks first among the evaluator's open-weight models. These figures use the same current index version, unlike older scores produced under previous benchmark mixes. They provide a useful starting point, not proof of equal quality on a particular repository, tool harness, document workflow, language, or modality.

Both launches clear the threshold for serious evaluation. Grok 4.7 materially improves coding-agent and knowledge-work results without increasing its base token rate. MiMo-V2.6 Pro combines a large capability jump with open weights, a one-million-token context window, and an unusually low hosted price. The buying question is therefore not which model won the launch week. It is whether a team values Grok's managed agent experience or MiMo's economics, modalities, and deployment control more.

Grok 4.7 is the simpler upgrade for hosted coding agents

SpaceXAI prices Grok 4.7 at $2 per million input tokens, $0.50 per million cached input tokens, and $6 per million output tokens for prompts below 200,000 tokens. It keeps the 500,000-token context window and low, medium, high, and xhigh reasoning settings of Grok 4.6. The public API also supports text and image input, tool use, and the Responses and Chat Completions APIs. For teams already using Grok 4.6, that makes 4.7 a direct model-name migration rather than an infrastructure project.

The clearest independent improvement is in agentic work. Artificial Analysis reports a Coding Agent Index score of 56 for Grok 4.7 with Grok Build, nine points above 4.6 and ahead of GPT-5.6 Sol in that evaluation. It also gained 111 Elo over Grok 4.6 high on AA-Briefcase, which tests long-horizon professional deliverables, and 90 Elo on GDPval-AA. SpaceXAI publishes additional benchmark wins, but those launch-table comparisons are vendor-reported and should be reproduced with the buyer's own harness.

The limitation is consumption. Artificial Analysis measured roughly 81,000 output tokens per Intelligence Index task at xhigh, versus 36,000 for Grok 4.6 high and 27,000 for GPT-6 Astra max. That can erase part of the attractive list price and increase latency. Grok also doubles to $4 input, $1 cached input, and $12 output for any request whose prompt reaches 200,000 tokens. Teams should test high as well as xhigh, track total task cost rather than token rates, and avoid feeding an entire repository when retrieval can supply a smaller working set.

MiMo-V2.6 Pro changes the open-weight price-performance frontier

Xiaomi's primary model card describes MiMo-V2.6 Pro as a sparse mixture-of-experts model with 1.02 trillion total parameters and 42 billion active per token. It supports a one-million-token context window and native text, image, video, and audio inputs. The weights are downloadable, and Artificial Analysis lists the release under the MIT license. That combination makes MiMo materially broader than a text-only open coding checkpoint, although the full model remains far too large for casual local use.

Independent results make the release significant rather than merely large. Artificial Analysis ranks MiMo-V2.6 Pro first among open-weight models at 46 on Intelligence Index v4.3.2. It reports about 125 to 130 output tokens per second, a 99% cache discount, and an evaluated cost of $0.13 per Intelligence Index task. Xiaomi's official overseas API table lists $0.435 input and $0.87 output per million tokens for the standard Pro endpoint. The same table offers a much more expensive UltraSpeed endpoint, so buyers must confirm the endpoint rather than assuming every deployment receives the headline throughput.

Xiaomi reports strong gains on DeepSWE, AutomationBench, tool use, computer use, and visual coding. Those benchmark numbers come from Xiaomi's model card and remain vendor-reported even when the benchmark names are public. Artificial Analysis independently confirms the composite intelligence result and GDPval standing, but it also describes the model as somewhat verbose. Self-hosting adds another caveat: the reference SGLang command spans two nodes with substantial tensor, data, and expert parallelism, while the vLLM instructions warn that stable serving support may lag. Open weights remove vendor lock-in; they do not remove hardware and operations cost.

Choose by deployment constraint, then validate total task cost

Choose Grok 4.7 first when the team wants a managed API, already uses Grok Build or a supported coding harness, and values a low-friction upgrade from 4.6. Its strongest evidence is in coding agents and polished professional deliverables. It is also the safer operational choice when nobody wants to own a trillion-parameter serving stack. The main evaluation risk is paying for xhigh verbosity or crossing the 200,000-token pricing threshold without a corresponding gain in successful outcomes.

Choose MiMo-V2.6 Pro first when open weights, private deployment, a one-million-token window, multimodal input, or very low API rates are hard requirements. Its hosted endpoint is the obvious starting point for most teams because it reveals whether model quality fits the workload before infrastructure spending begins. Self-host only when data control, customization, sovereignty, or sustained utilization can justify multi-GPU engineering and monitoring. MiMo-V2.6 Flash may eventually be the more practical self-hosted member of the family, but this comparison does not assume parity before independent results mature.

For either model, use twenty to fifty completed tasks drawn from real work. Include long-context cases, tool failures, repository changes, documents that require revision, and multimodal inputs if those matter. Record task success, reviewer time, retries, prompt and cached tokens, reasoning and output tokens, latency, tool calls, and the actual billed amount. Compare Grok high and xhigh rather than testing only the most expensive effort. Compare hosted MiMo before estimating self-hosting savings. A model wins when accepted work costs less or arrives more reliably, not when one composite score ties another.

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