Sarvam 105B (High) Intelligence, Performance & Price Analysis
Model summary
Verbosity
Sarvam 105B (High) is above average in intelligence and well priced when comparing to other open weight models of similar size. The model supports text input, outputs text, and has a 128k tokens context window.
Sarvam 105B (High) scores 9 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (median: 8).
Pricing for Sarvam 105B (High) is $0.04 per 1M input tokens (competitively priced, median: $0.30) and $0.17 per 1M output tokens (competitively priced, median: $0.80).
At 195 tokens per second, Sarvam 105B (High) is notably fast (94).
| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |
|---|---|
| Input modality | Supports: text |
| Output modality | Supports: text |
| Context window | 128k ~192 A4 pages of size 12 Arial font |
| Total parameters | 106B |
| Active parameters | 10.3B Number of parameters active per token during inference |
| License | Apache 2.0 |
| Model weights | Hugging Face |
Metrics are compared against models of the same class:
- Non-reasoning models → compared only with other non-reasoning models
- Reasoning models → compared across both reasoning and non-reasoning
- Open weights models → compared only with other open weights models of the same size class:
- Tiny: ≤4B parameters
- Small: 4B–40B parameters
- Medium: 40B–150B parameters
- Large: >150B parameters
- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
- <$0.15 per 1M tokens
- $0.15–$1 per 1M tokens
- >$1 per 1M tokens
Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Intelligence Index vs. Cost per Task
Evaluations
Agentic knowledge work, Elo
Knowledge
1 - hallucination rate
Agentic SaaS workflows
Agentic real-world work tasks, Elo
Reasoning & knowledge
Professional document reasoning, All-pass
Agentic coding & terminal use
Long context reasoning
Coding
Physics reasoning
Agentic scientific research workflows in a terminal
Agentic software engineering in open-source repositories
Quantitative analysis on spreadsheets & documents
Legal agentic work, Hallucination-Gated All-Pass Rate
Agentic business operations
Agentic interactive 3D experiences, Elo
Product and brand videos designed in code, Elo
Kubernetes incident root-cause analysis
Medical long context reasoning
Visual reasoning
Rich, varied, well-crafted language
Well-structured, scannable, answer-first
Truthful, acknowledges uncertainty, non-sycophantic
Use of filler and generic language
AA-Omniscience
AA-Omniscience Index
Openness Index
Artificial Analysis Openness Index: Score
Intelligence Index Comparisons
Intelligence Index vs. Cost per Task
Cost
Cost per Intelligence Index Task
Cost to Run Artificial Analysis Intelligence Index
Pricing: Cache Hit, Input, and Output
Context Window
Context Window
Speed
Measured by Output Speed (tokens per second)
Output Speed
Time per Intelligence Index Task
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
End-to-End Response Time
Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
End-to-End Response Time
Model Size (Open Weights Models Only)
Model Size: Total and Active Parameters
Frequently Asked Questions
Common questions about Sarvam 105B (High)
Sarvam 105B (High) was released on March 6, 2026.
Sarvam 105B (High) was created by Sarvam.
Sarvam 105B (High) scores 9 (estimated) on the Artificial Analysis Intelligence Index, placing it above average among other open weight models of similar size (median: 8).
Sarvam 105B (High) generates output at 195.1 tokens per second (based on Sarvam's API), which is well above average compared to other open weight models of similar size (median: 93.6 t/s).
Sarvam 105B (High) has a time to first token (TTFT) of 1.62s (based on Sarvam's API), which is better than average compared to other open weight models of similar size (median: 2.09s).
Sarvam 105B (High) costs $0.04 per 1M input tokens (very competitive, median: $0.30) and $0.17 per 1M output tokens (very competitive, median: $0.80), based on Sarvam's API.
Sarvam 105B (High) costs $0.04 per 1M input tokens and $0.17 per 1M output tokens (based on Sarvam's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.04 per 1M tokens. Pricing may vary by provider. Compare provider pricing
Yes, Sarvam 105B (High) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
Sarvam 105B (High) supports text input.
Sarvam 105B (High) supports text output.
No, Sarvam 105B (High) does not support image input. It can only process text.
No, Sarvam 105B (High) is not multimodal. It only supports text input.
Sarvam 105B (High) has a context window of 130k tokens. This determines how much text and conversation history the model can process in a single request.
Yes, Sarvam 105B (High) is open weights. The model weights are publicly available and can be downloaded for self-hosting.
Sarvam 105B (High) has 106 billion parameters (10.3 billion active).
Sarvam 105B (High) is a Mixture of Experts (MoE) model with 106 billion total parameters, but only 10.3 billion active parameters are used during inference.
Sarvam 105B (High) is released under the Apache 2.0 license. This license allows commercial use. View license
Sarvam 105B (High) achieves a score of 9 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, Sarvam 105B (High) is available via API through 1 provider. Compare API providers
Sarvam 105B (High) is available through 1 API provider. Compare providers