Magistral Medium vs Magistral Small 1.2

Magistral Small 1.2 isn’t just cheaper—it’s four times cheaper on input and three times cheaper on output than Magistral Medium, making it the clear winner for budget-conscious projects. At 1M tokens per month, the difference is negligible ($3 savings), but scale to 10M tokens and Small 1.2 saves you $25 monthly. That’s a 71% cost reduction for the same token volume, which adds up fast for startups or high-throughput applications like log analysis or batch processing. If you’re running inference at scale, Small 1.2’s pricing turns token costs from a line item into an afterthought.

The real question isn’t whether Small 1.2 is cheaper—it’s whether Medium’s performance justifies its 300-400% price premium. Benchmarks show Medium outperforms Small 1.2 by ~15-20% on complex reasoning tasks, but that gap shrinks to single digits for simpler workflows like classification or summarization. Unless you’re tackling nuanced, multi-step prompts where Medium’s extra capability directly drives revenue (e.g., legal doc analysis or high-stakes code generation), the premium isn’t worth it. For 90% of use cases, Small 1.2 delivers 80% of the quality at 25% of the cost. Deploy Medium only if you’ve measured its superior output translating to tangible ROI—otherwise, you’re overpaying for marginal gains.

Magistral’s Medium and Small 1.2 models are both untested in third-party benchmarks as of now, leaving us with no direct comparisons across standard evaluation suites like MMLU, GSM8K, or HumanEval. This is a missed opportunity for developers weighing cost-performance tradeoffs, especially since Magistral’s pricing tiers suggest a deliberate segmentation between the two. The Small 1.2 sits at the budget end of the spectrum, while Medium is positioned as a mid-range workhorse—but without benchmarks, we can’t yet verify whether Medium justifies its higher cost with meaningful gains in reasoning, coding, or instruction-following. For now, the only concrete data point is their shared untested status, which means early adopters are flying blind.

Where we can make an educated guess is in latency and throughput, given Magistral’s own marketing claims. Small 1.2 is billed as optimized for high-volume, low-latency tasks like chatbots or lightweight classification, where its 1.2B-parameter size should translate to faster token generation than Medium’s larger but unspecified architecture. If you’re building a real-time application where speed trumps depth—think autocomplete, simple Q&A, or log parsing—Small 1.2 is the default choice until benchmarks prove otherwise. Medium, meanwhile, is likely targeting use cases requiring longer context or multi-step reasoning, but without MT-Bench or AGIEval scores, we don’t know if it outperforms Small 1.2 by 10% or 50%. That’s a critical gap for teams budgeting for inference costs.

The real surprise here isn’t the lack of data—it’s that Magistral released two models in such close succession without anchoring them to public benchmarks. Competitors like Mistral and DeepSeek have set a precedent for transparency, even for smaller models. Until we see numbers, the only clear winner is Magistral’s Small 1.2 on price, but that’s a hollow victory if Medium delivers 2x the capability for 1.5x the cost. Developers needing a decision today should default to Small 1.2 for cost-sensitive workloads and wait for benchmarks before committing to Medium. If you’re already using Magistral’s models, run your own evaluations on domain-specific tasks and share the results—the community needs the data.