Llama 3.1 Swallow 8B Instruct vs Sarvam-M Multilingual Hybrid
Llama 3.1 Swallow 8B Instruct (2025) and Sarvam-M Multilingual Hybrid (2025) are compact production models from Tokyo Institute of Technology and Sarvam.ai. Llama 3.1 Swallow 8B Instruct ships a 4k-token context window, while Sarvam-M Multilingual Hybrid ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Sarvam-M Multilingual Hybrid fits 32x more tokens; pick it for long-context work and Llama 3.1 Swallow 8B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Swallow 8B Instruct | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | Long context |
| Context window | 4k | 128k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Use Llama 3.1 Swallow 8B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Sarvam-M Multilingual Hybrid has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Sarvam-M Multilingual Hybrid for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 Swallow 8B Instruct
Unavailable
No complete token price in local provider data
Sarvam-M Multilingual Hybrid
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-06-01 |
| Context window | 4k | 128k |
| Parameters | 8B | 24B |
| Architecture | Decoder Only | Decoder Only |
| License | Llama 2 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Unknown | Not released |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2023 | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Swallow 8B Instruct | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Swallow 8B Instruct | Sarvam-M Multilingual Hybrid |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| JSON / 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 scores are currently available for this pair.
Continue comparing
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Last reviewed: 2026-09-08. Data sourced from public model cards and provider documentation.