Agentic
Like-for-like- Gemini 3.1 Pro
- 38.9
- Supported · #121/152
- Grok 4.20
- 26.7
- Supported · #145/152
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Gemini 3.1 Pro leads · intervals overlap
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.1 Pro has the higher public score estimate, 69.95 versus 67.13, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
20 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.
Code generation, repair, and software-engineering tasks
Gemini 3.1 Pro
Gemini 3.1 Pro leads on the public coding lane, 46.3 to 28.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Tool use, computer use, and multi-step task completion
Gemini 3.1 Pro
Gemini 3.1 Pro leads on the public agentic lane, 38.9 to 26.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Prompts that approach the documented context limit
Grok 4.20
Grok 4.20 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Grok 4.20
Grok 4.20 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Gemini 3.1 Pro
Gemini 3.1 Pro has the lower estimated token cost for this stated workload. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Grok 4.20
Grok 4.20 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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 | Gemini 3.1 Pro | Grok 4.20 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 38.9Supported · #121/152 | 26.7Supported · #145/152 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Coding | 46.3Supported · #82/151 | 28.2Supported · #141/151 | Like-for-likeBenchAlign lane · 5 vs 6 public rows | Gemini 3.1 Pro leads |
| Knowledge | 66.1Supported · #21/183 | 49.4Supported · #85/183 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Multimodal | 79.2#12/48 | 34.6#43/48 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Gemini 3.1 Pro leads |
| Reasoning | 50.6Unranked · 2 rankable rows | 34.2Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Math | 54.3Unranked · 2 rankable rows | 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 |
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.
ARC-AGI-2
Reasoning
CharXiv
Multimodal
HLE w/o tools
Knowledge
MMMU-Pro
Multimodal
MMLU-Pro (Vals)
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
Grok 4.20 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Grok 4.20 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.1 Pro has the lower modeled cost
Grok 4.20 has no published cached-input rate, so cached tokens use its listed input 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.
Gemini 3.1 Pro
Grok 4.20
2M
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationGrok 4.20
Not sourced
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 4.20
Not published
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationGrok 4.20
Not sourced
Gemini 3.1 Pro
Grok 4.20
Not sourced
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogGrok 4.20
Not sourced
Gemini 3.1 Pro
Reasoning
Grok 4.20
Reasoning
Gemini 3.1 Pro
Proprietary
Grok 4.20
Proprietary
Gemini 3.1 Pro
Proprietary
Grok 4.20
Proprietary
Gemini 3.1 Pro
2026-02-19
Grok 4.20
2026-03-10
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Not directly comparable
DeepSearchQA
Shared sourceGemini 3.1 Pro leads this result
τ²-bench results
Not directly comparable
Gert Labs
Shared sourceGemini 3.1 Pro leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro leads this result
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench Pro
Shared sourceGemini 3.1 Pro leads this result
React Native Evals
Not directly comparable
Vibe Code Bench
Shared sourceGemini 3.1 Pro leads this result
LiveCodeBench (Vals)
Gemini 3.1 Pro leads this result
SWE-bench (Vals)
Gemini 3.1 Pro leads this result
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
ARC-AGI-2
Gemini 3.1 Pro leads this result
ARC-AGI-3
Shared sourceGemini 3.1 Pro leads this result
GPQA-D
Shared sourceGemini 3.1 Pro leads this result
HLE w/o tools
Shared sourceGemini 3.1 Pro leads this result
HealthBench Hard
Shared sourceGemini 3.1 Pro leads this result
MedXpertQA (Text)
Shared sourceGemini 3.1 Pro leads this result
GPQA Diamond (Vals)
Gemini 3.1 Pro leads this result
MMLU-Pro (Vals)
Gemini 3.1 Pro leads this result
MMMU-Pro
Shared sourceGemini 3.1 Pro leads this result
CharXiv
Shared sourceGemini 3.1 Pro leads this result
ERQA
Shared sourceGemini 3.1 Pro leads this result
SimpleVQA
Shared sourceGemini 3.1 Pro leads this result
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
Shared sourceGemini 3.1 Pro leads this result
Gemini 3.1 Pro has the higher public score estimate, 69.95 versus 67.13, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Gemini 3.1 Pro leads the public coding lane, 46.3 to 28.2, with Supported evidence for both models and non-overlapping 90% intervals.
Gemini 3.1 Pro leads the public agentic tasks lane, 38.9 to 26.7, with Supported evidence for both models, although the 90% intervals overlap.
For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.005 on Grok 4.20; repository review costs $0.136 and $0.118; the cache-heavy agent loop costs $0.2 and $0.5. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Grok 4.20 has the larger documented context window: 2M, compared with 1M.
Last updated September 10, 2026
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