Agentic
Like-for-like- MiniMax M2.7
- 57.0
- Ornith-1.0-397B
- 77.5
- Weighted basis
- 1 vs 1 rows
- Reading
- Ornith-1.0-397B leads
MiniMax M2.7 vs Ornith-1.0-397B
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.
5 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.
Agentic work
Tool use, computer use, and multi-step task completion
Ornith-1.0-397B
Ornith-1.0-397B leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Ornith-1.0-397B
Ornith-1.0-397B has the larger documented context window.
Confidence: documented
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
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.0-397B has no comparable published API token rate.
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.
13 MiniMax M2.7 only
5 Shared
2 Ornith-1.0-397B only
- Shared results
- 5
- MiniMax M2.7 only
- 13
- Ornith-1.0-397B only
- 2
- Like-for-like categories
- 1 / 8
1 category uses 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- MiniMax M2.7
- 53.3
- Ornith-1.0-397B
- 74.6
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M2.7
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | MiniMax M2.7 | Ornith-1.0-397B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 57.0 | 77.5 | Like-for-like1 vs 1 rows | Ornith-1.0-397B leads |
| Coding | 53.3 | 74.6 | Directional only2 vs 2 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 | Not measured | Not measured | Not comparable0 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 | Not measured | Not measured | Not comparable0 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
MiniMax M2.7: 57%Ornith-1.0-397B: 77.5%Normalized gap 20.5MiniMax M2.7 source Ornith-1.0-397B sourceSWE-bench Pro
Coding
MiniMax M2.7: 56.2%Ornith-1.0-397B: 62.2%Normalized gap 6.0MiniMax M2.7 source Ornith-1.0-397B 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
- MiniMax M2.7
- $0.0009
- Fits in one request
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ornith-1.0-397B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiniMax M2.7
- $0.0186
- Fits in one request
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ornith-1.0-397B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.0-397B 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.
SpecificationMiniMax M2.7Ornith-1.0-397B
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
Ornith-1.0-397B
256K
API model ID
MiniMax M2.7
Not sourced
Ornith-1.0-397B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiniMax M2.7
Not published
Ornith-1.0-397B
No comparable hosted API rate
Documented inputs
MiniMax M2.7
Not sourced
Ornith-1.0-397B
Not sourced
Documented outputs
MiniMax M2.7
Not sourced
Ornith-1.0-397B
Not sourced
Provider availability
MiniMax M2.7
Not sourced
Ornith-1.0-397B
Not sourced
Reasoning profile
MiniMax M2.7
Non-Reasoning
Ornith-1.0-397B
Reasoning
Weight access
MiniMax M2.7
Open Weight
Ornith-1.0-397B
Open Weight
License
MiniMax M2.7
Open Weight
Ornith-1.0-397B
Open Weight
Release date
MiniMax M2.7
2026-03-18
Ornith-1.0-397B
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
- Ornith-1.0-397B has the larger documented window (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 evidence20 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.757%SourceOrnith-1.0-397B77.5%SourceOrnith-1.0-397B leads this result
Toolathlon
MiniMax M2.746.3%SourceOrnith-1.0-397B—Not directly comparable
MLE-Bench Lite
MiniMax M2.766.6%SourceOrnith-1.0-397B—Not directly comparable
MM-ClawBench
MiniMax M2.762.7%SourceOrnith-1.0-397B—Not directly comparable
Claw-Eval
MiniMax M2.748.7%SourceOrnith-1.0-397B77.1%SourceOrnith-1.0-397B leads this result
Gert Labs
MiniMax M2.740.40%SourceOrnith-1.0-397B—Not directly comparable
Coding
SWE-bench Verified*
MiniMax M2.775.4%SourceOrnith-1.0-397B—Not directly comparable
SWE-bench Pro
MiniMax M2.756.2%SourceOrnith-1.0-397B62.2%SourceOrnith-1.0-397B leads this result
SWE-Rebench
MiniMax M2.751.9%SourceOrnith-1.0-397B—Not directly comparable
SWE Multilingual
MiniMax M2.776.5%SourceOrnith-1.0-397B78.9%SourceOrnith-1.0-397B leads this result
Multi-SWE Bench
MiniMax M2.752.7%SourceOrnith-1.0-397B—Not directly comparable
VIBE-Pro
MiniMax M2.755.6%SourceOrnith-1.0-397B—Not directly comparable
NL2Repo
MiniMax M2.739.8%SourceOrnith-1.0-397B48.2%SourceOrnith-1.0-397B leads this result
Vibe Code Bench
MiniMax M2.727.04%SourceOrnith-1.0-397B—Not directly comparable
React Native Evals
MiniMax M2.771.4%SourceOrnith-1.0-397B—Not directly comparable
SWE-bench Verified
MiniMax M2.7—Ornith-1.0-397B82.4%SourceNot directly comparable
Terminal-Bench 2.0
MiniMax M2.7—Ornith-1.0-397B77.5%SourceNot directly comparable
Knowledge
Math
AIME25 (Arcee)
MiniMax M2.780.0%SourceOrnith-1.0-397B—Not directly comparable
Frequently asked questions
Which is better, MiniMax M2.7 or Ornith-1.0-397B?
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, MiniMax M2.7 or Ornith-1.0-397B?
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, MiniMax M2.7 or Ornith-1.0-397B?
Ornith-1.0-397B leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, MiniMax M2.7 or Ornith-1.0-397B?
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, MiniMax M2.7 or Ornith-1.0-397B?
Ornith-1.0-397B has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated August 13, 2026
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