Agentic
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- 77.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Model comparison
MERaLiON-AudioLLM vs Ornith-1.0-397B
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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
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.
7 Ornith-1.0-397B only
- Shared results
- 0
- MERaLiON-AudioLLM only
- 0
- Ornith-1.0-397B only
- 7
- Like-for-like categories
- 0 / 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
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- 74.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- MERaLiON-AudioLLM
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | MERaLiON-AudioLLM | Ornith-1.0-397B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 77.5 | Not comparable0 vs 1 rows | Not comparable |
| Coding | Not measured | 74.6 | Not comparable0 vs 2 rows | Not comparable |
| 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- MERaLiON-AudioLLM
- API rate not published
- Fit state unavailable
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
MERaLiON-AudioLLM has no comparable published API token rate. Ornith-1.0-397B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MERaLiON-AudioLLM
- API rate not published
- Fit state unavailable
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
MERaLiON-AudioLLM has no comparable published API token rate. Ornith-1.0-397B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MERaLiON-AudioLLM
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MERaLiON-AudioLLM has no comparable published API token 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.
SpecificationMERaLiON-AudioLLMOrnith-1.0-397B
Context window
Maximum documented context; output-token limits may be lower.
MERaLiON-AudioLLM
N/A
Ornith-1.0-397B
256K
API model ID
MERaLiON-AudioLLM
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.
MERaLiON-AudioLLM
No comparable hosted API rate
AI Singapore model documentationOrnith-1.0-397B
No comparable hosted API rate
Documented inputs
MERaLiON-AudioLLM
Not sourced
Ornith-1.0-397B
Not sourced
Documented outputs
MERaLiON-AudioLLM
Not sourced
Ornith-1.0-397B
Not sourced
Provider availability
MERaLiON-AudioLLM
Not sourced
Ornith-1.0-397B
Not sourced
Reasoning profile
MERaLiON-AudioLLM
Non-Reasoning
Ornith-1.0-397B
Reasoning
Weight access
MERaLiON-AudioLLM
Open Weight
Ornith-1.0-397B
Open Weight
License
MERaLiON-AudioLLM
Open Weight
Ornith-1.0-397B
Open Weight
Release date
MERaLiON-AudioLLM
2024-12-13
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- A complete documented context comparison is not available.
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 evidence7 rows
Agentic
Coding
SWE-bench Verified
MERaLiON-AudioLLM—Ornith-1.0-397B82.4%SourceNot directly comparable
SWE-bench Pro
MERaLiON-AudioLLM—Ornith-1.0-397B62.2%SourceNot directly comparable
SWE Multilingual
MERaLiON-AudioLLM—Ornith-1.0-397B78.9%SourceNot directly comparable
NL2Repo
MERaLiON-AudioLLM—Ornith-1.0-397B48.2%SourceNot directly comparable
Terminal-Bench 2.0
MERaLiON-AudioLLM—Ornith-1.0-397B77.5%SourceNot directly comparable
Frequently asked questions
Which is better, MERaLiON-AudioLLM or Ornith-1.0-397B?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MERaLiON-AudioLLM or Ornith-1.0-397B?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, MERaLiON-AudioLLM or Ornith-1.0-397B?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, MERaLiON-AudioLLM 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, MERaLiON-AudioLLM or Ornith-1.0-397B?
A complete documented context-window comparison is not available.
Related comparisons
Last updated August 4, 2026
Watch MERaLiON-AudioLLM vs Ornith-1.0-397B
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.