Agentic
Like-for-like- MAI-Thinking-1
- 46.0
- Ornith-1.0-9B
- 43.1
- Weighted basis
- 1 vs 1 rows
- Reading
- MAI-Thinking-1 leads
MAI-Thinking-1 vs Ornith-1.0-9B
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
4 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.
Coding work
Code generation, repair, and software-engineering tasks
MAI-Thinking-1
MAI-Thinking-1 leads on the same 2 weighted benchmark rows.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
MAI-Thinking-1
MAI-Thinking-1 leads on the same 1 weighted benchmark row.
Confidence: limited
Show secondary and unsupported calls
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: listed-rates
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.
10 MAI-Thinking-1 only
4 Shared
3 Ornith-1.0-9B only
- Shared results
- 4
- MAI-Thinking-1 only
- 10
- Ornith-1.0-9B only
- 3
- Like-for-like categories
- 2 / 8
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
Like-for-like- MAI-Thinking-1
- 65.5
- Ornith-1.0-9B
- 59.2
- Weighted basis
- 2 vs 2 rows
- Reading
- MAI-Thinking-1 leads
Reasoning
Not comparable- MAI-Thinking-1
- Not measured
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MAI-Thinking-1
- 72.5
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 89.7
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not measured
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not measured
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- MAI-Thinking-1
- 85.0
- Ornith-1.0-9B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Ornith-1.0-9B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 46.0 | 43.1 | Like-for-like1 vs 1 rows | MAI-Thinking-1 leads |
| Coding | 65.5 | 59.2 | Like-for-like2 vs 2 rows | MAI-Thinking-1 leads |
| 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.
SWE-bench Pro
Coding
MAI-Thinking-1: 52.8%Ornith-1.0-9B: 42.9%Normalized gap 9.9MAI-Thinking-1 source Ornith-1.0-9B sourceSWE-bench Verified
Coding
MAI-Thinking-1: 73.5%Ornith-1.0-9B: 69.4%Normalized gap 4.1MAI-Thinking-1 source Ornith-1.0-9B sourceTerminal-Bench 2.0
Agentic
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
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Ornith-1.0-9B 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
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Ornith-1.0-9B 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
- Ornith-1.0-9B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MAI-Thinking-1 has no comparable published API token rate. Ornith-1.0-9B 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-1Ornith-1.0-9B
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Ornith-1.0-9B
256K
API model ID
MAI-Thinking-1
Not sourced
Ornith-1.0-9B
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
Ornith-1.0-9B
No comparable hosted API rate
Documented inputs
MAI-Thinking-1
Not sourced
Ornith-1.0-9B
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Ornith-1.0-9B
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Ornith-1.0-9B
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Ornith-1.0-9B
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Ornith-1.0-9B
Open Weight
License
MAI-Thinking-1
Proprietary
Ornith-1.0-9B
Open Weight
Release date
MAI-Thinking-1
2026-06-02
Ornith-1.0-9B
2026-06-01
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- 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 evidence17 rows
Agentic
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceOrnith-1.0-9B—Not directly comparable
SWE-bench Verified
MAI-Thinking-173.5%SourceOrnith-1.0-9B69.4%SourceMAI-Thinking-1 leads this result
SWE-bench Pro
MAI-Thinking-152.8%SourceOrnith-1.0-9B42.9%SourceMAI-Thinking-1 leads this result
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceOrnith-1.0-9B43.1%SourceMAI-Thinking-1 leads this result
SWE Multilingual
MAI-Thinking-1—Ornith-1.0-9B52%SourceNot directly comparable
NL2Repo
MAI-Thinking-1—Ornith-1.0-9B27.2%SourceNot directly comparable
Reasoning
Graphwalks BFS 128K
MAI-Thinking-190%SourceOrnith-1.0-9B—Not directly comparable
Knowledge
Math
Instruction following
IFBench
MAI-Thinking-185%SourceOrnith-1.0-9B—Not directly comparable
Frequently asked questions
Which is better, MAI-Thinking-1 or Ornith-1.0-9B?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MAI-Thinking-1 or Ornith-1.0-9B?
MAI-Thinking-1 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
Which is better for agentic tasks, MAI-Thinking-1 or Ornith-1.0-9B?
MAI-Thinking-1 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, MAI-Thinking-1 or Ornith-1.0-9B?
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 Ornith-1.0-9B?
Both models list the same context window, 256K.
Related comparisons
Last updated August 13, 2026
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