# Swap test shows GPT-5.5 and Kimi ace count

_AI Engineer swapped GPT-5.5, Kimi K2.6, DeepSeek V4 and Mistral Large 3; only two passed a 13-E test, grounding and vision split harder._

**Published:** 2026-10-19
**Source:** https://www.startuphub.ai/ai-news/artificial-intelligence/2026/swap-test-shows-gpt-5-5-and-kimi-ace-count

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[AI Engineer](https://www.youtube.com/watch?v=UGjk3Uk0MxM) pitted GPT-5.5 and three challengers against a 13-E counting trap and only two cleared it on the first pass according to the workshop.

The host ran the four models on a shared Microsoft Foundry project with env-based routing and warned the token capacity could trigger 429 rate-limit errors during the live swap, so alternative deployments were staged and colleague Cedric was on hand to troubleshoot.

On RAG grounding with a carpenter-bee-only corpus, GPT-5.5 correctly said the sources lacked honey-bee nest details, [DeepSeek](https://www.startuphub.ai/ai-news/artificial-intelligence/2026/china-s-moonshot-ai-sparks-market-jitters) V4 Flash did the same, and Mistral Large 3 answered from weights and still leaked honey-bee details after a stricter cite-or-say-no-source prompt according to AI Engineer.

For multimodal images GPT-5.5 and Kimi K2.6 correctly called the horned animal an aurochs not a unicorn, identified crocodiles by snout, and found the 58-cent cheapest plant, while Mistral Large 3 mislabeled the crocodiles as alligators and picked a 2 dollars 30 cents plant and DeepSeek V4 Flash was excluded as text-only according to the workshop.

Tool calling widened the gap as GPT-5.5 produced clean calendar-event arguments and solved a shop total in one calculator call, while DeepSeek and Mistral added the requester as an attendee or malformed the location, and price matters here because DeepSeek Flash drops from 0 point 30 dollars to 0 point 006 dollars per million tokens on a cache hit at peak rates per Layer3Labs, a cost axis the demos did not benchmark.

The workshop proves prompt engineering can rescue weaker models but its single-run anecdotes do not show latency, price per task or repeatability, a controlled eval with error bars would change the read, and the open question is whether teams should pay for precision or prompt around it.

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Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory.

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