Updated November 10, 2026. Rank says Grok 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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.
Gemma 4 26B A4B: No comparable API price
Grok 4.6: $2 input / $6 output
Gemma 4 26B A4B: 256K
Grok 4.6: 500K
Gemma 4 26B A4B: April 2, 2026
Grok 4.6: August 12, 2026
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.
Code generation, repair, and software-engineering tasks
Grok 4.6
Grok 4.6 has the higher public coding point estimate, 58.6 to 31.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Grok 4.6
Grok 4.6 has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
Grok 4.6 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 26B A4B | Grok 4.6 | Basis | Reading |
|---|---|---|---|---|
| Coding | 31.9Supported · #100/158 | 58.6Supported · #23/158 | Like-for-likeBenchAlign v5.8 lane · 2 vs 9 public rows | Grok 4.6 leads |
| Reasoning | 35.2Supported · #103/158 | 65.0Supported · #22/158 | Like-for-likeBenchAlign v5.8 lane · 1 vs 3 public rows | Grok 4.6 leads |
| Knowledge | 41.4Estimated · #110/179 | 67.9Supported · #21/179 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Agentic | Not ranked | 69.7Supported · #11/132 | Not comparableBenchAlign v5.8 lane · 0 vs 7 public rows | Not comparable |
| Multimodal | 48.7Supported · #59/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 88.7#37/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 65.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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 26B A4B has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 26B A4B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Gemma 4 26B A4B 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 26B A4B
Grok 4.6
Gemma 4 26B A4B
gemma-4-26b-a4b-it
Google Gemma Gemini API guideGrok 4.6
grok-4.6
xAI Grok 4.6 release notesA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 26B A4B
No comparable hosted API rate
Grok 4.6
$0.5 per 1M cached input tokens
xAI Grok 4.6 release notesGemma 4 26B A4B
text, image
Google Gemma 4 model documentationGrok 4.6
text, image
xAI model page for grok-4.6Gemma 4 26B A4B
Grok 4.6
Gemma 4 26B A4B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGrok 4.6
Not sourced
Gemma 4 26B A4B
Reasoning
Grok 4.6
Reasoning
Gemma 4 26B A4B
Open Weight
Grok 4.6
Proprietary
Gemma 4 26B A4B
Open Weight
Grok 4.6
Proprietary
Gemma 4 26B A4B
2026-04-02
Grok 4.6
2026-08-12
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.
Grok 4.6 has the higher public coding point estimate, 58.6 to 31.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Grok 4.6 has the larger documented context window: 500K, compared with 256K.
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 4.1
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
APEX-Agents
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
Not directly comparable
ScarfBench
Not directly comparable
LiveCodeBench v6
Not directly comparable
Bug Hunt Bench
Not directly comparable
DeepSWE
Not directly comparable
CursorBench 3.2
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
VulcanBench v3
Not directly comparable
FrontierSWE v2
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
CursorBench 4.0
Not directly comparable
GPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated November 10, 2026
