Sarvam-M vs o4-mini-deep-research
o4-mini-deep-research comes out ahead, 49 to 6 on our weighted score, and it is the cheaper option too.
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
- Our pick
OpenAIo4-mini-deep-research
49/100- ECI—
- Price$1.80 / $7.20
- Context200K
Add a model
Make it a three-way comparison.
o4-mini-deep-research is our pick
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Sarvam-M (6). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceo4-mini-deep-researcho4-mini-deep-research $3.15 per 1M tokens (3:1 blend) · Sarvam-M unpriced
- Longest contexto4-mini-deep-researcho4-mini-deep-research 200,000 · Sarvam-M 32,768 tokens
- Widest inputso4-mini-deep-researchSarvam-M: Text · o4-mini-deep-research: Text, Images
- Self-hostingSarvam-MPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Sarvam-M | o4-mini-deep-research |
|---|---|---|---|
| Inputs & features | 60% | 10 | 60 |
| Context window | 40% | 0 | 32 |
| Overall | 100% | 6/100 | 49/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Sarvam-MSarvam AI | o4-mini-deep-researchOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $1.80 |
| Output | — | $7.20 |
| Cached input | — | — |
| Blended (3:1) | — | $3.15 |
| Long-context rate | — | Same rate |
| Price source | — | Median of 1 providers |
| Limits | ||
| Context window | 32,768 tokens | 200,000 tokens (best) |
| Max output | 32,768 tokens | 100,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | No | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | Proprietary |
| API model ID | — | — |
| API providers | — | 1 |
| Released | May 20, 2025 | Jun 26, 2025 |
| Knowledge cutoff | — | May 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Sarvam-M—
- o4-mini-deep-research$32.40
Which should you choose?
Which is better: Sarvam-M or o4-mini-deep-research?
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Sarvam-M (6). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Sarvam-M or o4-mini-deep-research?
o4-mini-deep-research is cheaper at $1.80 input / $7.20 output per million tokens (median across 1 API provider). . At a typical mix of three input tokens to one output token, that is $3.15 per million tokens for o4-mini-deep-research versus . Sarvam-M has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Sarvam-M has not been scored yet and o4-mini-deep-research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam-M and o4-mini-deep-research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sarvam-M does not support tool calling, which most coding agents need.
Which has the bigger context window?
o4-mini-deep-research has the largest context window at 200,000 tokens, against 32,768 for Sarvam-M. Maximum output per response: Sarvam-M up to 32,768, o4-mini-deep-research up to 100,000 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; o4-mini-deep-research accepts text and images. o4-mini-deep-research handles the widest range of inputs.
Are any of these open source?
Sarvam-M publishes its weights (Apache 2.0) and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
o4-mini-deep-research is the newest, released Jun 26, 2025. Sarvam-M came out May 20, 2025. Knowledge cutoff: o4-mini-deep-research May 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
