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Model comparison

MAI-Thinking-1 vs Mistral Small 4

Data verified

Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

No sourced benchmark result is currently shared by both models. This page therefore compares only the available metadata, pricing, and runtime rows; it does not name a quality winner.
N/A
No comparison
46.23/100
0 category wins0 category wins

Public leaderboard positions: MAI-Thinking-1 unranked (Not scored); Mistral Small 4 #140 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. MAI-Thinking-1 and Mistral Small 4 share 0 comparable benchmark results. 0 of 8 categories are comparable. 13 results are unique to MAI-Thinking-1; 16 to Mistral Small 4.

Updated July 23, 2026
Shared results
0
MAI-Thinking-1 only
13
Mistral Small 4 only
16
Comparable categories
0 / 8

Benchmark data for MAI-Thinking-1 and Mistral Small 4 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.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricMAI-Thinking-1Mistral Small 4Comparison
Input / output priceUSD per 1M tokensMAI-Thinking-1Not availableMistral Small 4$0.15 input / $0.6 outputA complete price comparison is not available.
Generation speedtokens per secondMAI-Thinking-1Not availableMistral Small 4175 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenMAI-Thinking-1Not availableMistral Small 40.64 sA complete latency comparison is not available.
Context windowmaximum listed tokensMAI-Thinking-1256KMistral Small 4256KListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkMAI-Thinking-1Mistral Small 4Result
Terminal-Bench 2.0Source 46%Not comparable
AA Agentic IndexSource 4.7%Not comparable
τ²-bench resultsSource 41.2%Not comparable
GDPval-AASource 4.4%Not comparable
GDPval-AASource 588Not comparable
Coding
BenchmarkMAI-Thinking-1Mistral Small 4Result
SWE-bench VerifiedSource 73.5%Not comparable
SWE-bench ProSource 52.8%Not comparable
Terminal-Bench 2.0Source 46.0%Not comparable
AA Coding IndexSource 26.6%Not comparable
AA-SciCodeSource 38.0%Not comparable
Reasoning
BenchmarkMAI-Thinking-1Mistral Small 4Result
Graphwalks BFS 128KSource 90%Not comparable
AA-LCRSource 44.7%Not comparable
CritPtSource 0.3%Not comparable
Knowledge
BenchmarkMAI-Thinking-1Mistral Small 4Result
GPQASource 84.2%Not comparable
GPQA-DSource 84.2%Not comparable
MMLU-ProSource 85%Not comparable
SimpleQASource 31%Not comparable
Artificial Analysis Intelligence IndexSource 19.6%Not comparable
AA-GPQA DiamondSource 76.9%Not comparable
AA-HLESource 9.5%Not comparable
AA-Omniscience IndexSource -29.9%Not comparable
AA-Omniscience AccuracySource 22.1%Not comparable
AA-Omniscience Hallucination RateSource 66.8%Not comparable
Math
BenchmarkMAI-Thinking-1Mistral Small 4Result
AIME 2025Source 97%Not comparable
AIME26Source 94.5%Not comparable
HMMT Feb 2026Source 84.9%Not comparable
Multimodal
BenchmarkMAI-Thinking-1Mistral Small 4Result
AA-MMMU-ProSource 56.8%Not comparable
Inst. Following
BenchmarkMAI-Thinking-1Mistral Small 4Result
IFBenchSource 85%Not comparable
AA-IFBenchSource 48.2%Not comparable
Frequently Asked Questions (3)

Can I compare MAI-Thinking-1 and Mistral Small 4 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 MAI-Thinking-1 and Mistral Small 4 today?

Mistral Small 4: $0.15 input / $0.60 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

MAI-Thinking-1
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Mistral Small 4
API / mo$563
Self-host / mo$2,278
Break-even266M/day
Model the full break-even

Related Comparisons

Last updated: July 23, 2026

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