Coding work
Code generation, repair, and software-engineering tasks
Grok 4.6
Grok 4.6 leads on the public coding lane, 62 to 43.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Gemini 3.1 Pro vs Grok 4.6
Updated September 25, 2026. Rank says Grok 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Grok 4.6 has the higher public score estimate, 69.19 versus 64.45, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- Gemini 3.1 Pro only
- 21
- Grok 4.6 only
- 10
- Like-for-like categories
- 3 / 8
Estimated: Gemini 3.1 Pro · Supported: Grok 4.6How the comparison works
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.
Agentic work
Tool use, computer use, and multi-step task completion
Grok 4.6
Grok 4.6 leads on the public agentic lane, 67.9 to 39.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: strongerLong documents
Prompts that approach the documented context limit
Gemini 3.1 Pro
Gemini 3.1 Pro has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
Grok 4.6
Grok 4.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Grok 4.6
Grok 4.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
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.
43.3Gemini 3.1 Pro62.0Grok 4.6
Like-for-like · BenchAlign v5.7
Grok 4.6 leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
21 Gemini 3.1 Pro only
7 Shared
10 Grok 4.6 only
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.
ARC-AGI-2Reasoning
Normalized gap 10.0Gemini 3.1 Pro:77.1%Grok 4.6:67.1%ARC-AGI-3Reasoning
Normalized gap 1.7Gemini 3.1 Pro:0.4%Grok 4.6:2.1%MMLU-Pro (Vals)Knowledge
Normalized gap 1.6Gemini 3.1 Pro:91.0%Grok 4.6:89.4%LiveCodeBench (Vals)Coding
Normalized gap 0.3Gemini 3.1 Pro:88.5%Grok 4.6:88.2%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Like-for-like- Gemini 3.1 Pro
- 39.3
- Supported · #47/105
- Grok 4.6
- 67.9
- Supported · #8/105
- Basis
- BenchAlign v5.7 lane · 6 vs 4 public rows
- Reading
- Grok 4.6 leads
Coding
Like-for-like- Gemini 3.1 Pro
- 43.3
- Supported · #52/135
- Grok 4.6
- 62.0
- Supported · #14/135
- Basis
- BenchAlign v5.7 lane · 5 vs 8 public rows
- Reading
- Grok 4.6 leads
Knowledge
Like-for-like- Gemini 3.1 Pro
- 66.7
- Supported · #18/158
- Grok 4.6
- 69.0
- Supported · #13/158
- Basis
- BenchAlign v5.7 lane · 6 vs 2 public rows
- Reading
- Grok 4.6 leads · intervals overlap
Reasoning
Not comparable- Gemini 3.1 Pro
- 54.4
- Unranked · 2 rankable rows
- Grok 4.6
- 57.6
- #16/19
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.1 Pro
- 79.1
- #13/50
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.1 Pro
- Not ranked
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.1 Pro
- Not ranked
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.1 Pro
- 54.2
- Unranked · 2 rankable rows
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.1 Pro | Grok 4.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 39.3Supported · #47/105 | 67.9Supported · #8/105 | Like-for-likeBenchAlign v5.7 lane · 6 vs 4 public rows | Grok 4.6 leads |
| Coding | 43.3Supported · #52/135 | 62.0Supported · #14/135 | Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows | Grok 4.6 leads |
| Knowledge | 66.7Supported · #18/158 | 69.0Supported · #13/158 | Like-for-likeBenchAlign v5.7 lane · 6 vs 2 public rows | Grok 4.6 leads · intervals overlap |
| Reasoning | 54.4Unranked · 2 rankable rows | 57.6#16/19 | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multimodal | 79.1#13/50 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | 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 |
| Math | 54.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
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
- Gemini 3.1 Pro
- $0.008
- Fits in one request
- Grok 4.6
- $0.005
- Fits in one request
Grok 4.6 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.1 Pro
- $0.136
- Fits in one request
- Grok 4.6
- $0.118
- Fits in one request
Grok 4.6 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.1 Pro
- $0.2
- Fits in one request
- Grok 4.6
- $0.2
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.1 ProGrok 4.6
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
Grok 4.6
API model ID
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationGrok 4.6
grok-4.6
xAI Grok 4.6 release notesCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingGrok 4.6
$0.5 per 1M cached input tokens
xAI Grok 4.6 release notesDocumented inputs
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationGrok 4.6
Not sourced
Documented outputs
Gemini 3.1 Pro
Grok 4.6
Not sourced
Provider availability
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogGrok 4.6
Not sourced
Reasoning profile
Gemini 3.1 Pro
Reasoning
Grok 4.6
Reasoning
Weight access
Gemini 3.1 Pro
Proprietary
Grok 4.6
Proprietary
License
Gemini 3.1 Pro
Proprietary
Grok 4.6
Proprietary
Release date
Gemini 3.1 Pro
2026-02-19
Grok 4.6
2026-08-12
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
- Grok 4.6 has the higher public score estimate, 69.19 versus 64.45, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.136 vs $0.118. Cache-heavy agent loop: $0.2 vs $0.2.
- Context tradeoff
- Gemini 3.1 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 3.1 Pro or Grok 4.6?
Grok 4.6 has the higher public score estimate, 69.19 versus 64.45, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 3.1 Pro or Grok 4.6?
Grok 4.6 leads the public coding lane, 62 to 43.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 3.1 Pro or Grok 4.6?
Grok 4.6 leads the public agentic tasks lane, 67.9 to 39.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Gemini 3.1 Pro or Grok 4.6?
For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.005 on Grok 4.6; repository review costs $0.136 and $0.118; the cache-heavy agent loop costs $0.2 and $0.2. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.1 Pro or Grok 4.6?
Gemini 3.1 Pro has the larger documented context window: 1M, compared with 500K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Claw-Eval
Gemini 3.1 Pro57.8%SourceGrok 4.6—Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%SourceGrok 4.6—Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%SourceGrok 4.6—Not directly comparable
Gert Labs
Gemini 3.1 Pro56.87%SourceGrok 4.6—Not directly comparable
ResearchClawBench
Gemini 3.1 Pro13.3%SourceGrok 4.6—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%SourceGrok 4.678.3%SourceGrok 4.6 leads this result
Terminal-Bench 3.0
Gemini 3.1 Pro—Grok 4.626.5%SourceNot directly comparable
APEX-Agents
Gemini 3.1 Pro—Grok 4.657.5%SourceNot directly comparable
ApprenticeBench
Gemini 3.1 Pro—Grok 4.613%SourceNot directly comparable
Coding
LiveCodeBench Pro
Gemini 3.1 Pro82.9%SourceGrok 4.6—Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%SourceGrok 4.6—Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%SourceGrok 4.6—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%SourceGrok 4.688.2%SourceGemini 3.1 Pro leads this result
SWE-bench (Vals)
Gemini 3.1 Pro78.8%SourceGrok 4.695.6%SourceGrok 4.6 leads this result
Bug Hunt Bench
Gemini 3.1 Pro—Grok 4.627 fixesSourceNot directly comparable
DeepSWE
Gemini 3.1 Pro—Grok 4.665.9%SourceNot directly comparable
cursorBench32
Gemini 3.1 Pro—Grok 4.670.8%SourceNot directly comparable
FrontierCode 1.1 Extended
Gemini 3.1 Pro—Grok 4.661.3%SourceNot directly comparable
VulcanBench v3
Gemini 3.1 Pro—Grok 4.687.0%SourceNot directly comparable
FrontierSWE v2
Gemini 3.1 Pro—Grok 4.625.3%SourceNot directly comparable
Reasoning
Multimodal
MMMU-Pro
Gemini 3.1 Pro83.9%SourceGrok 4.6—Not directly comparable
CharXiv
Gemini 3.1 Pro80.2%SourceGrok 4.6—Not directly comparable
ERQA
Gemini 3.1 Pro69.4%SourceGrok 4.6—Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%SourceGrok 4.6—Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%SourceGrok 4.6—Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%SourceGrok 4.6—Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%SourceGrok 4.6—Not directly comparable
Knowledge
GPQA-D
Gemini 3.1 Pro94.3%SourceGrok 4.6—Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%SourceGrok 4.6—Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%SourceGrok 4.6—Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%SourceGrok 4.6—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%SourceGrok 4.694.7%SourceGemini 3.1 Pro leads this result
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%SourceGrok 4.689.4%SourceGemini 3.1 Pro leads this result
Math
38 public results · 7 shared
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Last updated September 25, 2026