Agentic
Directional only- Gemma 4 E2B
- 45.3
- Estimated · #94/151
- Mellum2-12B-A2.5B-Thinking
- 48.3
- Estimated · #76/151
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Directional only
Gemma 4 E2B 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
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.
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
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
Gemma 4 E2B 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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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. Gemma 4 E2B does not fit this workload in one request. Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. Gemma 4 E2B has no comparable published API token rate. Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
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.
1 Gemma 4 E2B only
1 Shared
5 Mellum2-12B-A2.5B-Thinking only
- Shared results
- 1
- Gemma 4 E2B only
- 1
- Mellum2-12B-A2.5B-Thinking only
- 5
- 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- Gemma 4 E2B
- 37.7
- Estimated · #149/181
- Mellum2-12B-A2.5B-Thinking
- 48.8
- Estimated · #96/181
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Directional only
Coding
Not comparable- Gemma 4 E2B
- 40.8
- Estimated · #138/183
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- BenchAlign lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemma 4 E2B
- 33.2
- 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- Gemma 4 E2B
- Not ranked
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 E2B
- Not ranked
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 E2B
- 25.8
- 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- Gemma 4 E2B
- 43.8
- #95/120
- Mellum2-12B-A2.5B-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 E2B | Mellum2-12B-A2.5B-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | 45.3Estimated · #94/151 | 48.3Estimated · #76/151 | Directional onlyBenchAlign lane · 0 vs 1 public rows | Directional only |
| Knowledge | 37.7Estimated · #149/181 | 48.8Estimated · #96/181 | Directional onlyBenchAlign lane · 2 vs 3 public rows | Directional only |
| Coding | 40.8Estimated · #138/183 | Not ranked | Not comparableBenchAlign lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 33.2Unranked · 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 | 25.8Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 43.8#95/120 | 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.
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
Gemma 4 E2B: 43.4%Mellum2-12B-A2.5B-Thinking: 57.6%Normalized gap 14.2Gemma 4 E2B source Mellum2-12B-A2.5B-Thinking 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
- Gemma 4 E2B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Fits in one request
Gemma 4 E2B has no comparable published API token rate. Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 E2B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Fits in one request
Gemma 4 E2B has no comparable published API token rate. Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 E2B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Mellum2-12B-A2.5B-Thinking
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
Gemma 4 E2B does not fit this workload in one request. Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. Gemma 4 E2B has no comparable published API token 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.
SpecificationGemma 4 E2BMellum2-12B-A2.5B-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 E2B
Mellum2-12B-A2.5B-Thinking
128K
API model ID
Gemma 4 E2B
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.
Gemma 4 E2B
No comparable hosted API rate
Mellum2-12B-A2.5B-Thinking
No comparable hosted API rate
Documented inputs
Gemma 4 E2B
text, image, audio, video
Google Gemma 4 model documentationMellum2-12B-A2.5B-Thinking
Not sourced
Documented outputs
Gemma 4 E2B
Mellum2-12B-A2.5B-Thinking
Not sourced
Provider availability
Gemma 4 E2B
Open Weights · open weights
Google gemma-4-E2B model cardMellum2-12B-A2.5B-Thinking
Not sourced
Reasoning profile
Gemma 4 E2B
Reasoning
Mellum2-12B-A2.5B-Thinking
Reasoning
Weight access
Gemma 4 E2B
Open Weight
Mellum2-12B-A2.5B-Thinking
Open Weight
License
Gemma 4 E2B
Open Weight
Mellum2-12B-A2.5B-Thinking
Open Weight
Release date
Gemma 4 E2B
2026-04-02
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
- 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
- Both models list 128K.
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
BFCL v4
Gemma 4 E2B—Mellum2-12B-A2.5B-Thinking45.6%SourceNot directly comparable
Coding
LiveCodeBench v6
Gemma 4 E2B—Mellum2-12B-A2.5B-Thinking69.9%SourceNot directly comparable
Knowledge
GPQA
Gemma 4 E2B43.4%SourceMellum2-12B-A2.5B-Thinking57.6%SourceMellum2-12B-A2.5B-Thinking leads this result
MMLU-Pro
Gemma 4 E2B60%SourceMellum2-12B-A2.5B-Thinking—Not directly comparable
MMLU-Redux
Gemma 4 E2B—Mellum2-12B-A2.5B-Thinking86.2%SourceNot directly comparable
GPQA-D
Gemma 4 E2B—Mellum2-12B-A2.5B-Thinking57.6%SourceNot directly comparable
Instruction following
IFEval
Gemma 4 E2B—Mellum2-12B-A2.5B-Thinking76.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemma 4 E2B or Mellum2-12B-A2.5B-Thinking?
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, Gemma 4 E2B 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, Gemma 4 E2B or Mellum2-12B-A2.5B-Thinking?
Mellum2-12B-A2.5B-Thinking scores higher for agentic tasks on the public lane, 48.3 to 45.3. Gemma 4 E2B 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, Gemma 4 E2B 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, Gemma 4 E2B or Mellum2-12B-A2.5B-Thinking?
Both models list the same context window, 128K.
Related comparisons
Last updated September 4, 2026
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