Llama 3.3 Nemotron Super 49B v1 vs Xiaomi MiMo-V2.5-TTS-Series
Llama 3.3 Nemotron Super 49B v1 (2025) and Xiaomi MiMo-V2.5-TTS-Series (2026) are compact production models from NVIDIA AI and Xiaomi. Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window, while Xiaomi MiMo-V2.5-TTS-Series ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Xiaomi MiMo-V2.5-TTS-Series is safer overall; choose Llama 3.3 Nemotron Super 49B v1 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.3 Nemotron Super 49B v1 | Xiaomi MiMo-V2.5-TTS-Series |
|---|---|---|
| Best for | general production evaluation | multimodal apps |
| Decision fit | Long context | Vision |
| Context window | 128k | — |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.3 Nemotron Super 49B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
- Xiaomi MiMo-V2.5-TTS-Series uniquely exposes Multimodal in local model data.
- Local decision data tags Xiaomi MiMo-V2.5-TTS-Series for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.3 Nemotron Super 49B v1
Unavailable
No complete token price in local provider data
Xiaomi MiMo-V2.5-TTS-Series
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.3 Nemotron Super 49B v1 and Xiaomi MiMo-V2.5-TTS-Series; plan for SDK, billing, or endpoint changes.
- Xiaomi MiMo-V2.5-TTS-Series adds Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Xiaomi MiMo-V2.5-TTS-Series and Llama 3.3 Nemotron Super 49B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2026-04-23 |
| Context window | 128k | — |
| Parameters | 49B | — |
| Architecture | decoder only | - |
| License | NVIDIA Open Model | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | Xiaomi MiMo-V2.5-TTS-Series |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | Xiaomi MiMo-V2.5-TTS-Series |
|---|---|---|
| Vision | No | No |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Xiaomi MiMo-V2.5-TTS-Series. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Llama 3.3 Nemotron Super 49B v1 has no token price sourced yet and Xiaomi MiMo-V2.5-TTS-Series has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.3 Nemotron Super 49B v1 when provider fit are central to the workload. Choose Xiaomi MiMo-V2.5-TTS-Series when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Is Llama 3.3 Nemotron Super 49B v1 or Xiaomi MiMo-V2.5-TTS-Series open source?
Llama 3.3 Nemotron Super 49B v1 is listed under NVIDIA Open Model. Xiaomi MiMo-V2.5-TTS-Series is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for multimodal input, Llama 3.3 Nemotron Super 49B v1 or Xiaomi MiMo-V2.5-TTS-Series?
Xiaomi MiMo-V2.5-TTS-Series has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3.3 Nemotron Super 49B v1 and Xiaomi MiMo-V2.5-TTS-Series?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Xiaomi MiMo-V2.5-TTS-Series is available on Xiaomi. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Llama 3.3 Nemotron Super 49B v1 over Xiaomi MiMo-V2.5-TTS-Series?
Xiaomi MiMo-V2.5-TTS-Series is safer overall; choose Llama 3.3 Nemotron Super 49B v1 when provider fit matters. If your workload also depends on provider fit, start with Llama 3.3 Nemotron Super 49B v1; if it depends on provider fit, run the same evaluation with Xiaomi MiMo-V2.5-TTS-Series.
Continue comparing
Last reviewed: 2026-05-26. Data sourced from public model cards and provider documentation.