Coding
Like-for-like- MiniMax M3
- 72.2
- Ornith-1.0-397B
- 74.6
- Weighted basis
- 2 vs 2 rows
- Reading
- Ornith-1.0-397B leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated 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.
6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Ornith-1.0-397B
Ornith-1.0-397B leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
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
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
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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | MiniMax M3 | Ornith-1.0-397B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 72.2 | 74.6 | Like-for-like2 vs 2 rows | Ornith-1.0-397B leads |
| Agentic | 72.3 | 77.5 | Directional only3 vs 1 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 85.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 64.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
SWE-bench Pro
Coding
SWE-bench Verified
Coding
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.
1K fresh input + 500 output tokens
Ornith-1.0-397B has no comparable published API token rate.
50K fresh input + 3K output tokens
Ornith-1.0-397B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Ornith-1.0-397B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
MiniMax M3
1M
Ornith-1.0-397B
256K
MiniMax M3
Not sourced
Ornith-1.0-397B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiniMax M3
$0.06 per 1M cached input tokens
Ornith-1.0-397B
No comparable hosted API rate
MiniMax M3
Not sourced
Ornith-1.0-397B
Not sourced
MiniMax M3
Not sourced
Ornith-1.0-397B
Not sourced
MiniMax M3
Not sourced
Ornith-1.0-397B
Not sourced
MiniMax M3
Non-Reasoning
Ornith-1.0-397B
Reasoning
MiniMax M3
Open Weight
Ornith-1.0-397B
Open Weight
MiniMax M3
Open Weight
Ornith-1.0-397B
Open Weight
MiniMax M3
2026-06-01
Ornith-1.0-397B
2026-06-01
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Ornith-1.0-397B leads this result
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Ornith-1.0-397B leads this result
BankerToolBench
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
SWE-bench Verified
Ornith-1.0-397B leads this result
SWE-bench Pro
Ornith-1.0-397B leads this result
Terminal-Bench 2.0
Ornith-1.0-397B leads this result
NL2Repo
Ornith-1.0-397B leads this result
VIBE V2
Not directly comparable
SVG-Bench
Not directly comparable
KernelBench Hard
Not directly comparable
EEBench
Not directly comparable
SWE Multilingual
Not directly comparable
USAMO 2026
Not directly comparable
OfficeQA Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
MMMU-Pro
Not directly comparable
VideoMMMU
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
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.
Ornith-1.0-397B leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
MiniMax M3 has the larger documented context window: 1M, compared with 256K.
Last updated August 13, 2026
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