Agentic
Directional only- MAI-Thinking-1
- 46.0
- Step 3.7 Flash
- 66.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Model comparison
MAI-Thinking-1 vs Step 3.7 Flash
Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
MAI-Thinking-1 has the higher public score estimate, 51.03 versus 49.89, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
11 MAI-Thinking-1 only
3 Shared
8 Step 3.7 Flash only
- Shared results
- 3
- MAI-Thinking-1 only
- 11
- Step 3.7 Flash only
- 8
- Like-for-like categories
- 0 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- MAI-Thinking-1
- 65.5
- Step 3.7 Flash
- 56.3
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Reasoning
Not comparable- MAI-Thinking-1
- Not measured
- Step 3.7 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MAI-Thinking-1
- 72.5
- Step 3.7 Flash
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 89.7
- Step 3.7 Flash
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not measured
- Step 3.7 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not measured
- Step 3.7 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- MAI-Thinking-1
- 85.0
- Step 3.7 Flash
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Step 3.7 Flash | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 46.0 | 66.4 | Directional only1 vs 2 rows | Directional only |
| Coding | 65.5 | 56.3 | Directional only2 vs 1 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 72.5 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Math | 89.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 85.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
Terminal-Bench 2.0
Agentic
MAI-Thinking-1: 46%Step 3.7 Flash: 59.5%Normalized gap 13.5MAI-Thinking-1 source Step 3.7 Flash sourceSWE-bench Pro
Coding
MAI-Thinking-1: 52.8%Step 3.7 Flash: 56.3%Normalized gap 3.5MAI-Thinking-1 source Step 3.7 Flash source
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Step 3.7 Flash
- $0.00077
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Step 3.7 Flash
- $0.01345
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Step 3.7 Flash
- $0.0555
- Fits in one request
- Cached input priced at the published list-input rate
Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. MAI-Thinking-1 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMAI-Thinking-1Step 3.7 Flash
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Step 3.7 Flash
256K
API model ID
MAI-Thinking-1
Not sourced
Step 3.7 Flash
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MAI-Thinking-1
No comparable hosted API rate
Step 3.7 Flash
Not published
Documented inputs
MAI-Thinking-1
Not sourced
Step 3.7 Flash
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Step 3.7 Flash
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Step 3.7 Flash
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Step 3.7 Flash
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Step 3.7 Flash
Open Weight
License
MAI-Thinking-1
Proprietary
Step 3.7 Flash
Open Weight
Release date
MAI-Thinking-1
2026-06-02
Step 3.7 Flash
2026-05-29
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- MAI-Thinking-1 has the higher public score estimate, 51.03 versus 49.89, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence22 rows
Agentic
Terminal-Bench 2.0
MAI-Thinking-146%SourceStep 3.7 Flash59.5%SourceStep 3.7 Flash leads this result
BrowseComp
MAI-Thinking-1—Step 3.7 Flash75.8%SourceNot directly comparable
DeepSearchQA
MAI-Thinking-1—Step 3.7 Flash92.8%SourceNot directly comparable
Toolathlon
MAI-Thinking-1—Step 3.7 Flash49.5%SourceNot directly comparable
Claw-Eval
MAI-Thinking-1—Step 3.7 Flash67.1%SourceNot directly comparable
HLE w/ tools
MAI-Thinking-1—Step 3.7 Flash47.2%SourceNot directly comparable
Gert Labs
MAI-Thinking-1—Step 3.7 Flash51.57%SourceNot directly comparable
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceStep 3.7 Flash—Not directly comparable
SWE-bench Verified
MAI-Thinking-173.5%SourceStep 3.7 Flash—Not directly comparable
SWE-bench Pro
MAI-Thinking-152.8%SourceStep 3.7 Flash56.3%SourceStep 3.7 Flash leads this result
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceStep 3.7 Flash59.5%SourceStep 3.7 Flash leads this result
Reasoning
Graphwalks BFS 128K
MAI-Thinking-190%SourceStep 3.7 Flash—Not directly comparable
Knowledge
Math
Multimodal
Instruction following
IFBench
MAI-Thinking-185%SourceStep 3.7 Flash—Not directly comparable
Frequently asked questions
Which is better, MAI-Thinking-1 or Step 3.7 Flash?
MAI-Thinking-1 has the higher public score estimate, 51.03 versus 49.89, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, MAI-Thinking-1 or Step 3.7 Flash?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, MAI-Thinking-1 or Step 3.7 Flash?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, MAI-Thinking-1 or Step 3.7 Flash?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, MAI-Thinking-1 or Step 3.7 Flash?
Both models list the same context window, 256K.
Related comparisons
Last updated August 7, 2026
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