Agentic
Not comparable- Mellum2-12B-A2.5B-Thinking
- 48.3
- Estimated · #76/151
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 1 vs 0 public rows
- Reading
- Not comparable
Mellum2-12B-A2.5B-Thinking vs o1-pro
Updated September 4, 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.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Long documents
Prompts that approach the documented context limit
o1-pro
o1-pro 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
Mellum2-12B-A2.5B-Thinking and o1-pro are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O1-pro is not ranked on the public lane for agentic, so no winner is named for agentic.
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. Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate. Mellum2-12B-A2.5B-Thinking 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.
5 Mellum2-12B-A2.5B-Thinking only
1 Shared
- Shared results
- 1
- Mellum2-12B-A2.5B-Thinking only
- 5
- o1-pro only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 1 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Mellum2-12B-A2.5B-Thinking
- 48.8
- Estimated · #96/181
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- Not comparable
Math
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mellum2-12B-A2.5B-Thinking
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mellum2-12B-A2.5B-Thinking | o1-pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 48.3Estimated · #76/151 | Not ranked | Not comparableBenchAlign lane · 1 vs 0 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 1 vs 0 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 48.8Estimated · #96/181 | Not ranked | Not comparableBenchAlign lane · 3 vs 1 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
GPQA
Knowledge
Mellum2-12B-A2.5B-Thinking: 57.6%o1-pro: 79%Normalized gap 21.4Mellum2-12B-A2.5B-Thinking source o1-pro 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
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Fits in one request
- o1-pro
- $0.45
- Fits in one request
Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Fits in one request
- o1-pro
- $9.30
- Fits in one request
Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
- o1-pro
- $39.00
- Does not fit in one request
- Cached input priced at the published list-input rate
Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. o1-pro does not fit this workload in one request. o1-pro has no published cached-input rate, so cached tokens use its listed input rate. Mellum2-12B-A2.5B-Thinking 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.
SpecificationMellum2-12B-A2.5B-Thinkingo1-pro
Context window
Maximum documented context; output-token limits may be lower.
Mellum2-12B-A2.5B-Thinking
128K
o1-pro
200K
API model ID
Mellum2-12B-A2.5B-Thinking
Not sourced
o1-pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mellum2-12B-A2.5B-Thinking
No comparable hosted API rate
o1-pro
Not published
Documented inputs
Mellum2-12B-A2.5B-Thinking
Not sourced
o1-pro
Not sourced
Documented outputs
Mellum2-12B-A2.5B-Thinking
Not sourced
o1-pro
Not sourced
Provider availability
Mellum2-12B-A2.5B-Thinking
Not sourced
o1-pro
Not sourced
Reasoning profile
Mellum2-12B-A2.5B-Thinking
Reasoning
o1-pro
Reasoning
Weight access
Mellum2-12B-A2.5B-Thinking
Open Weight
o1-pro
Proprietary
License
Mellum2-12B-A2.5B-Thinking
Open Weight
o1-pro
Proprietary
Release date
Mellum2-12B-A2.5B-Thinking
2026-05-28
o1-pro
2024-12-01
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
- o1-pro has the larger documented window (200K).
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 evidence6 rows
Frequently asked questions
Which is better, Mellum2-12B-A2.5B-Thinking or o1-pro?
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, Mellum2-12B-A2.5B-Thinking or o1-pro?
Mellum2-12B-A2.5B-Thinking and o1-pro are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Mellum2-12B-A2.5B-Thinking or o1-pro?
O1-pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Mellum2-12B-A2.5B-Thinking or o1-pro?
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, Mellum2-12B-A2.5B-Thinking or o1-pro?
o1-pro has the larger documented context window: 200K, compared with 128K.
Related comparisons
Last updated September 4, 2026
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