Decision summary
- Choose Sarvam 105B for Indian-language and code-mixed enterprise workflows.
- Choose DeepSeek-R1 when your own evals favor general reasoning, coding, or open-weight ecosystem fit.
- Treat the numbers as a Sarvam-published benchmark snapshot, then rerun with your prompts.
Model
Sarvam 105B vs DeepSeek-R1
Sarvam 105B and DeepSeek-R1 are both reasoning-oriented open-weight model families, but they serve different decision lanes.
Updated 8 weeks ago · Last verified: August 2026
Choose Sarvam 105B when
India-focused multilingual assistants, long-context document analysis, and agentic workflows needing local-language fidelity.
Choose DeepSeek-R1 when
General reasoning, math, code, and open-weight experimentation where Indian-language specialization is not the main requirement.
- Choose Sarvam 105B for Indian-language and code-mixed enterprise workflows.
- Choose DeepSeek-R1 when your own evals favor general reasoning, coding, or open-weight ecosystem fit.
- Treat the numbers as a Sarvam-published benchmark snapshot, then rerun with your prompts.
Decision axes: Best fit · Language fit · Reasoning and math · Coding
How they compare
Criterion-by-criterion notes from the catalog—not a ranking. Validate on your own gold set.
| Criterion | Sarvam 105B | DeepSeek-R1 |
|---|---|---|
| Best fit | India-focused multilingual assistants, long-context document analysis, and agentic workflows needing local-language fidelity. | General reasoning, math, code, and open-weight experimentation where Indian-language specialization is not the main requirement. |
| Language fit | Sarvam reports 90% average win rate on its Indian-language benchmark and support for native script, romanized, and code-mixed inputs. | Not positioned primarily as an Indic model; evaluate separately for Indian languages, romanized prompts, and code-mixed usage. |
| Reasoning and math | Sarvam-reported: Math500 98.6, AIME25 88.3 / 96.7 with tools, HMMT Feb 2025 85.8, GPQA Diamond 78.7. | Sarvam table for DeepSeek R1 0528 reports AIME25 87.5, HMMT Feb 2025 79.4, and GPQA Diamond 81.0. |
| Coding | Sarvam-reported: LiveCodeBench v6 71.7 and SWE-Bench Verified 45.0. | Sarvam table for DeepSeek R1 0528 reports LiveCodeBench v6 73.3 and SWE-Bench Verified 57.6. |
| Agentic benchmarks | Sarvam-reported: BrowseComp 49.5 and Tau2 average 68.3. | Sarvam table reports BrowseComp 3.2 and Tau2 62.0. |
| Context and API | 128K context window, streaming OpenAI-compatible chat completions API. | Use current DeepSeek docs or host-specific limits for context and API details; they vary by serving path. |
| Deployment and governance | Apache 2.0 open weights, Sarvam cloud/API, and Sarvam enterprise positioning around India-based deployment options. | Open-weight reasoning model family with broad ecosystem adoption; governance depends on host, region, and safety policy. |
| Pricing signal | Public Sarvam pricing lists Sarvam 105B at INR 4 input, INR 2.5 cached input, INR 16 output per 1M tokens. | Pricing depends on the selected DeepSeek API or third-party hosting provider. |
| Evidence caveat | Metrics are from Sarvam's official benchmark blog; Indian-language benchmark is Sarvam-defined and uses LLM-as-judge methodology. | Comparison metrics here come from Sarvam's benchmark table, not an independent cross-lab audit. |
Key insights
Concrete technical or product signals.
- Sarvam 105B is the stronger fit when Indic language fidelity and India deployment posture are core requirements.
- DeepSeek-R1 remains a stronger general coding baseline in the Sarvam-reported table for LiveCodeBench and SWE-Bench Verified.
- The most honest production answer is routing: Sarvam for Indian-language lanes, DeepSeek-R1 where your own evals show stronger general reasoning or coding economics.
Use cases
Where this shines in production.
- Choosing a model lane for Indian-language enterprise assistants.
- Benchmarking open-weight reasoning models for coding and agentic workflows.
- Designing a model router that separates Indian-language UX from global coding/reasoning workloads.
Limitations & trade-offs
What to watch for.
- The benchmark numbers are Sarvam-reported and should be independently reproduced before procurement.
- DeepSeek-R1 values refer to the DeepSeek R1 0528 row in Sarvam's table, while the site entity is the broader DeepSeek-R1 catalog entry.
- Pricing, context, safety behavior, and regional availability can change by host and deployment path.
Operational risks
What to validate before rollout.
Final recommendation
What to do after reading the table.
Pilot both if the workload is high value. Route Indian-language and India-specific workflows to Sarvam 105B when it wins your eval; keep DeepSeek-R1 in the candidate set for general reasoning and coding tasks.
FAQ
Is Sarvam 105B better than DeepSeek-R1?
Pilot both if the workload is high value. Route Indian-language and India-specific workflows to Sarvam 105B when it wins your eval; keep DeepSeek-R1 in the candidate set for general reasoning and coding tasks.
Which is better for coding: Sarvam 105B or DeepSeek-R1?
Sarvam 105B: Sarvam-reported: LiveCodeBench v6 71.7 and SWE-Bench Verified 45.0. DeepSeek-R1: Sarvam table for DeepSeek R1 0528 reports LiveCodeBench v6 73.3 and SWE-Bench Verified 57.6. The catalog does not declare a winner on coding.
Which is cheaper: Sarvam 105B or DeepSeek-R1?
Listed unit prices: Sarvam 105B Public Sarvam pricing lists Sarvam 105B at INR 4 input, INR 2.5 cached input, INR 16 output per 1M tokens; DeepSeek-R1 Pricing depends on the selected DeepSeek API or third-party hosting provider. A lower listed unit price is not the same as a lower total workload cost—tokens, session minutes, caching, introductory rates, and add-on modes can reverse the ranking. Confirm live rates on each vendor’s official page.
Which is better for business workflows?
Sarvam 105B: Apache 2.0 open weights, Sarvam cloud/API, and Sarvam enterprise positioning around India-based deployment options. DeepSeek-R1: Open-weight reasoning model family with broad ecosystem adoption; governance depends on host, region, and safety policy. The catalog does not declare a winner on deployment and governance.
Can I use both Sarvam 105B and DeepSeek-R1?
Yes. Many teams keep both and route by constraint. Use Sarvam 105B for India-focused multilingual assistants, long-context document analysis, and agentic workflows needing local-language fid…, and DeepSeek-R1 for General reasoning, math, code, and open-weight experimentation where Indian-language specialization is not the main req…. Confirm vendor terms before mixing them in one production workflow.