Model comparison
Granite-4.0-1B vs MAI-Thinking-1
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. Granite-4.0-1B and MAI-Thinking-1 share 0 comparable benchmark results. 0 of 8 categories are comparable. 11 results are unique to Granite-4.0-1B; 13 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 0
- Granite-4.0-1B only
- 11
- MAI-Thinking-1 only
- 13
- Comparable categories
- 0 / 8
Benchmark data for Granite-4.0-1B and MAI-Thinking-1 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
MAI-Thinking-1 has the larger context window at 256K, compared with 128K for Granite-4.0-1B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Granite-4.0-1B | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Granite-4.0-1B$0 input / $0 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Granite-4.0-1BNot available | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Granite-4.0-1BNot available | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Granite-4.0-1B128K | MAI-Thinking-1256K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
Agentic2 benchmarks
Coding4 benchmarks
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | Granite-4.0-1B | MAI-Thinking-1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 2.1% | — | Not comparable |
| AA-GPQA DiamondSource | 28.1% | — | Not comparable |
| AA-HLESource | 5.1% | — | Not comparable |
| AA-Omniscience IndexSource | -81.8% | — | Not comparable |
| AA-Omniscience AccuracySource | 6.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 93.5% | — | Not comparable |
| GPQASource | — | 84.2% | Not comparable |
| GPQA-DSource | — | 84.2% | Not comparable |
| MMLU-ProSource | — | 85% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
Math3 benchmarks
Frequently Asked Questions (3)
Can I compare Granite-4.0-1B and MAI-Thinking-1 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Granite-4.0-1B and MAI-Thinking-1 today?
Granite-4.0-1B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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