Agentic
Directional only- GPT-5.2-Codex
- 52.4
- Estimated · #46/151
- Mellum2-12B-A2.5B-Thinking
- 48.3
- Estimated · #76/151
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Directional only
GPT-5.2-Codex vs Mellum2-12B-A2.5B-Thinking
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
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 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
GPT-5.2-Codex
GPT-5.2-Codex 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 is 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
GPT-5.2-Codex and Mellum2-12B-A2.5B-Thinking are scored on Estimated evidence for agentic, so the reading is 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. Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. GPT-5.2-Codex 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.
3 GPT-5.2-Codex only
6 Mellum2-12B-A2.5B-Thinking only
- Shared results
- 0
- GPT-5.2-Codex only
- 3
- Mellum2-12B-A2.5B-Thinking only
- 6
- Like-for-like categories
- 0 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Directional only- GPT-5.2-Codex
- 57.8
- Estimated · #46/181
- Mellum2-12B-A2.5B-Thinking
- 48.8
- Estimated · #96/181
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.2-Codex
- 52.5
- Supported · #54/183
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- BenchAlign lane · 1 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2-Codex
- 78.0
- Unranked · 2 rankable rows
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2-Codex
- Not ranked
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2-Codex
- Not ranked
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2-Codex
- 72.4
- Unranked · 1 rankable row
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2-Codex
- 93.5
- #3/120
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2-Codex | Mellum2-12B-A2.5B-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | 52.4Estimated · #46/151 | 48.3Estimated · #76/151 | Directional onlyBenchAlign lane · 2 vs 1 public rows | Directional only |
| Knowledge | 57.8Estimated · #46/181 | 48.8Estimated · #96/181 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Coding | 52.5Supported · #54/183 | Not ranked | Not comparableBenchAlign lane · 1 vs 1 public rows | Not comparable |
| Reasoning | 78.0Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted 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 | 72.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.5#3/120 | 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.
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
- GPT-5.2-Codex
- $0.00875
- Fits in one request
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Fits in one request
Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.2-Codex
- $0.1295
- Fits in one request
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- 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
- GPT-5.2-Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. GPT-5.2-Codex 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.
SpecificationGPT-5.2-CodexMellum2-12B-A2.5B-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2-Codex
400K
Mellum2-12B-A2.5B-Thinking
128K
API model ID
GPT-5.2-Codex
Not sourced
Mellum2-12B-A2.5B-Thinking
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2-Codex
Not published
Mellum2-12B-A2.5B-Thinking
No comparable hosted API rate
Documented inputs
GPT-5.2-Codex
Not sourced
Mellum2-12B-A2.5B-Thinking
Not sourced
Documented outputs
GPT-5.2-Codex
Not sourced
Mellum2-12B-A2.5B-Thinking
Not sourced
Provider availability
GPT-5.2-Codex
Not sourced
Mellum2-12B-A2.5B-Thinking
Not sourced
Reasoning profile
GPT-5.2-Codex
Reasoning
Mellum2-12B-A2.5B-Thinking
Reasoning
Weight access
GPT-5.2-Codex
Proprietary
Mellum2-12B-A2.5B-Thinking
Open Weight
License
GPT-5.2-Codex
Proprietary
Mellum2-12B-A2.5B-Thinking
Open Weight
Release date
GPT-5.2-Codex
2025-12-18
Mellum2-12B-A2.5B-Thinking
2026-05-28
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
- GPT-5.2-Codex has the larger documented window (400K).
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 evidence9 rows
Agentic
Coding
Knowledge
Instruction following
IFEval
GPT-5.2-Codex—Mellum2-12B-A2.5B-Thinking76.5%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.2-Codex or Mellum2-12B-A2.5B-Thinking?
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, GPT-5.2-Codex or Mellum2-12B-A2.5B-Thinking?
Mellum2-12B-A2.5B-Thinking is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.2-Codex or Mellum2-12B-A2.5B-Thinking?
GPT-5.2-Codex scores higher for agentic tasks on the public lane, 52.4 to 48.3. GPT-5.2-Codex and Mellum2-12B-A2.5B-Thinking are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.2-Codex or Mellum2-12B-A2.5B-Thinking?
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, GPT-5.2-Codex or Mellum2-12B-A2.5B-Thinking?
GPT-5.2-Codex has the larger documented context window: 400K, compared with 128K.
Related comparisons
Last updated September 4, 2026
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