Agentic
Directional only- Gemma 4 E4B
- 45.9
- Estimated · #91/151
- Qwen3.6-27B
- 33.9
- Supported · #133/151
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.6-27B has the higher public score estimate, 52.73 versus 42.92, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 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
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.
Prompts that approach the documented context limit
Qwen3.6-27B
Qwen3.6-27B has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E4B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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
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 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.
Confidence: listed-rates
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Gemma 4 E4B | Qwen3.6-27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 45.9Estimated · #91/151 | 33.9Supported · #133/151 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Coding | 41.5Estimated · #131/183 | 42.6Supported · #128/183 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Knowledge | 39.3Estimated · #143/181 | 49.0Estimated · #95/181 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Instruction following | 51.8#77/120 | 82.2#50/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 44.7Unranked · 2 rankable rows | 73.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 72.8Unranked · 5 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 35.5Unranked · 1 rankable row | 51.5#35/48 | Not comparableProvisional lane · 0 vs 2 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.
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
MMLU-Pro
Knowledge
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.
1K fresh input + 500 output tokens
Gemma 4 E4B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 E4B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Gemma 4 E4B
Qwen3.6-27B
262K
Gemma 4 E4B
Not sourced
Qwen3.6-27B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 E4B
No comparable hosted API rate
Qwen3.6-27B
No comparable hosted API rate
Gemma 4 E4B
text, image, audio, video
Google Gemma 4 model documentationQwen3.6-27B
Not sourced
Gemma 4 E4B
Qwen3.6-27B
Not sourced
Gemma 4 E4B
Open Weights · open weights
Google gemma-4-E4B model cardQwen3.6-27B
Not sourced
Gemma 4 E4B
Reasoning
Qwen3.6-27B
Reasoning
Gemma 4 E4B
Open Weight
Qwen3.6-27B
Open Weight
Gemma 4 E4B
Open Weight
Qwen3.6-27B
Open Weight
Gemma 4 E4B
2026-04-02
Qwen3.6-27B
2026-04-21
Run the same representative tasks against both endpoints before changing production traffic.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
QwenWebBench
Not directly comparable
AndroidWorld
Not directly comparable
Gert Labs
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench
Not directly comparable
NL2Repo
Not directly comparable
GPQA
Qwen3.6-27B leads this result
MMLU-Pro
Qwen3.6-27B leads this result
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
C-Eval
Not directly comparable
HLE
Not directly comparable
HMMT Feb 2025
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
AIME26
Not directly comparable
MMMU
Not directly comparable
MMMU-Pro
Not directly comparable
RealWorldQA
Not directly comparable
DynaMath
Not directly comparable
MStar
Not directly comparable
SimpleVQA
Not directly comparable
CharXiv
Not directly comparable
CC-OCR
Not directly comparable
CountBench
Not directly comparable
RefCOCO (avg)
Not directly comparable
ERQA
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
VideoMMMU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
V*
Not directly comparable
Qwen3.6-27B has the higher public score estimate, 52.73 versus 42.92, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Qwen3.6-27B scores higher for coding on the public lane, 42.6 to 41.5. Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Gemma 4 E4B scores higher for agentic tasks on the public lane, 45.9 to 33.9. Gemma 4 E4B is 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Qwen3.6-27B has the larger documented context window: 262K, compared with 128K.
Last updated September 4, 2026
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.