MIRA

돌비콩의 투자정복6h ago

🤔 Same AI model, but performance differs by 2x? #sponsored source 1. In August 2025, a math benchmark was conducted on OpenAI's gpt-oss-120b model with interesting results. For the exact same model, depending on which provider you use, the accuracy rate differed from 36.7% to 93.3%. In other words, the same model name doesn't guarantee the same performance. 2. AI model performance varies depending on the provider's environment that runs it. Some cut costs by compressing models or failing to properly apply settings. Some even have tool-calling functionality that doesn't work normally, but from the user's perspective, it's provided with the same model name so it's hard to notice. 3. Sterling, launched by Mira Network, starts from here. It's an AI inference service that directly tests providers, selects only those that pass verification, and provides AI inference through verified providers. 4. The usage method is designed by changing existing API keys and access addresses. As a launch benefit, Claude, GPT, and Gemini models are provided ~50% cheaper, and supports USDC (x402) and $MIRA token payments. As AI model performance competition intensifies, there's growing demand for services that verify whether that performance is actually being delivered, which is interesting to see.

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(주)청년열정민트초코6h ago

Mira's 'Sterling' - same AI model but why such performance gap? (ad) Same service name but different performance Why are there so many API providers offering AI models cheap these days. Interesting thing is: even using the same model doesn't guarantee same performance. Specifically, when multiple vendors ran gpt-oss-120b, an OpenAI open-weight model, to solve math problems, the AIME25 benchmark correct-answer rate ranged from 36.7% to 93.3%, according to the report. To serve AI models, you upload the model to GPU and run it, but in this process companies sometimes compress the model or adjust inference settings to cut costs. This means from the user's perspective: you select the same model and pay, but actually get totally different quality answers from different vendors. The problem is hard to know this difference beforehand when using an API. So the service Mira launched - 'Sterling' Mira Network originally developed technology to cross-validate AI-generated answers across multiple models. This time they've launched Sterling, an AI inference service that validates model providers themselves and provides inference services only through vendors passing quality standards. Basically: don't just trust the company that built the AI model, but also verify the vendor actually running it properly. Plus they don't just end at vendor performance comparison—they directly connect you to use AI inference services from these validated providers. With AI agents calling models dozens or hundreds of times, answer quality and stability really matter, so verification is definitely needed in this important area. Mira is expanding their business from validating AI answers to managing AI infrastructure execution quality. Sterling: https://sterling.mira.network/ (how to use): log in, review provided AI models and usage terms, get API key → connect to agents #MIRA #KOL

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쿠쿠쿱6h ago

Mira Network launches AI inference verification service 'Sterling' #ad 1. Launch background: Critical performance variance across API providers • Even with the same AI model, actual performance varies significantly depending on the provider offering the API and the execution environment. Some providers secretly serve smaller models or have tool calls functionality breaking issues. • In the gpt-oss-120b benchmark, provider scores ranged from 36.7% to 93.3%, and investigation of 189 endpoints over 4 months found 37 undisclosed changes at 29 locations. • The problem is that regular users only see the model name, making it difficult to verify such quality degradation or arbitrary changes. 2. Solution: 'Sterling' connecting only verified providers • Mira Network launched 'Sterling', an inference service that continuously tests AI model providers around the clock and routes requests only through providers passing verification (currently in beta). • 5 key verification criteria: actual model match against reference, tool calls functioning properly, parameter adherence, silent failures, declared context length support. • Test scores and justification data for each model and provider are all disclosed transparently, and providers failing verification are pushed to the back of routing priority. 3. Usage method and benefits • Easy integration: No need to modify existing code, just get one API key and change the Base URL for immediate application. • Cost and payment: For launch promotion, Claude, GPT, Gemini models are provided up to 50% cheaper than official pricing. Payment is pay-as-you-go, and supports USDC or $MIRA token payments through x402. Pay only for what you use. Mira X official notice / Sterling website

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잼민1236h ago

Same Name, Different AI—Can You Trust It? #AD It's said that even with the same model, the actual provider serving it is different. Some run smaller versions or have broken tool calls. ✅ MIRA Sterling - Pre-tests serving providers - Routes requests only to those that pass - Lets you actually use the model you chose MIRA Sterling tests providers first and connects only through verified ones. How which AI you choose matters as much as where it runs. 🔗 Source: https://x.com/miranetwork/status/2118252068855333067

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