Model Comparison
3.5 (1210)
vs. 105B (high)
Comparing 2 AI models · 11 benchmarks · OpenChat, Sarvam
Recommended Pick
Sa105B (high)5 metric winsStrongest on: Throughput, Reasoning, Intelligence
Lowest Price
3.5 (1210)
$0.00/1M input price
Best Reasoning
105B (high)
31.9 reasoning score
Blends available reasoning benchmarks
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Price gap
3.5 (1210) is ∞x cheaper on input tokens than 105B (high).
Reasoning gap
105B (high) leads 3.5 (1210) by 19.6 points on reasoning.
Top-pick rationale
105B (high) wins 5 measurable categories, including Throughput, Reasoning, Intelligence, GPQA.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
3.5 (1210)
OpenChat
TTFT
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Time
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tok/s
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Tokens
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Cost
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105B (high)
Sarvam
TTFT
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Time
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tok/s
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Tokens
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Cost
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Which answer was more useful?
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Full Comparison
| Metric | Op 3.5 (1210) | Top Pick Sa 105B (high) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.00/1M | $0.04/1M |
| Output Cost | $0.00/1M | $0.17/1M |
| Blended (3:1) | — | $0.07/1M |
| Specifications | ||
| Organization | OpenChat | Sarvam |
| Release Date | Dec 18, 2023 | Mar 6, 2026 |
| Performance & Speed | ||
| Throughput | — | 142.3 tok/s |
| TTFT | — | 1168ms |
| Latency | — | 15224ms |
| Composite Indices | ||
| Value Score | — | 100.0 |
| Reasoning Score | 12.3 | 31.9 |
| Intelligence | 3.0 | 11.9 |
| Standard Benchmarks | ||
| GPQA | 23.0% | 73.8% |
| MMLU Pro | 31.0% | — |
| HLE | 4.8% | 10.1% |
| LiveCodeBench | 11.5% | — |
| MATH 500 | 30.7% | — |
| AIME (Original) | 0.0% | — |
| SciCode | — | 26.4% |
| LCR | — | 0.0% |
| IFBench | — | 34.4% |
| TAU-bench v2 | — | 46.8% |
| TerminalBench Hard | — | 1.5% |
Key Takeaways
3.5 (1210) offers the best value at $0.00/1M,making it ideal for high-volume applications and cost-conscious projects.
105B (high) has the strongest reasoning profile with a 31.9 reasoning score,combining the available reasoning-heavy benchmarks.
All models support context windows of ∞+ tokens,suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
3.5 (1210)
- Cost-sensitive applications
- High-volume processing
105B (high)
- Complex reasoning tasks
- Research & analysis
