Model comparison
Gemini 3.1 Pro vs Grok 4.7
Google against SpaceXAI, compared on context, price, and verified benchmark results.
Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
Gemini 3.1 Pro
Frontier
SpaceXAI
Grok 4.7
Frontier
| Specification | Gemini 3.1 Pro | Grok 4.7 |
|---|---|---|
| Provider | ||
| Provider | SpaceXAI | |
| Tier | ||
| Tier | Frontier | Frontier |
| Context window | ||
| Context window | Winner: 1.05M | 500K |
| Max output | ||
| Max output | 66K | Not verifiedUnverified |
| Input / 1M tokens | ||
| Input / 1M tokens | $2 | $2 |
| Output / 1M tokens | ||
| Output / 1M tokens | $12 | Winner: $6 |
| Weights | ||
| Weights | Closed | Closed |
| Parameters | ||
| Parameters | Not disclosedUnverified | new, larger base model with longer RL on multi-hour tasks |
| Reasoning levels | ||
| Reasoning levels | low, medium, high | low, medium, high, xhigh |
| Modalities | ||
| Modalities | text, image, video, audio, pdf | text, image |
| API model id | ||
| API model id | gemini-3.1-pro-preview | grok-4.7 |
| Released | ||
| Released | February 19, 2026 | September 21, 2026 |
| Artificial Analysis Intelligence Index (2026-09-26) | ||
| Artificial Analysis Intelligence Index (2026-09-26) | 29.7 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [xhigh] (2026-09-26) | ||
| Artificial Analysis Intelligence Index [xhigh] (2026-09-26) | Not verifiedUnverified | 46.4 |
| SWE-bench Verified (2026-02) | ||
| SWE-bench Verified (2026-02) | 80.6 | Not verifiedUnverified |
| GPQA Diamond (2026-02) | ||
| GPQA Diamond (2026-02) | 94.3 | Not verifiedUnverified |
| Humanity's Last Exam (2026-02) | ||
| Humanity's Last Exam (2026-02) | 44.4 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-07) | ||
| Terminal-Bench 2.1 (2026-07) | 73.8 | Not verifiedUnverified |
| DeepSWE 1.1 [high] (2026-09-21) | ||
| DeepSWE 1.1 [high] (2026-09-21) | Not verifiedUnverified | 71 |
| Terminal-Bench 4.0 [xhigh] (2026-09-26) | ||
| Terminal-Bench 4.0 [xhigh] (2026-09-26) | Not verifiedUnverified | 25.8 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- 2026-09-26Artificial Analysis Terminal-Bench 4.0 (independent AA run, part of Intelligence Index v4.3.2)
Agentic terminal work: long multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNOT comparable with Terminal-Bench 2.x or 3.0 — 4.0 uses a new, non-overlapping task set. Within 4.0, scores from different harnesses (tbench.ai agent entries vs Artificial Analysis runs) are not interchangeable.
- 2026-09-26Artificial Analysis
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.
- 2026-09-21xAI — Grok 4.7 announcement (vendor; not yet on Datacurve's board)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- 2026-07Google DeepMind model evaluation report (vendor, Terminus-2; own Feb report separately reported TB 2.0 = 68.5 — versions preserved)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- 2026-02Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)
Real GitHub issue resolution: does the model's patch pass the hidden tests.
Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
Pricing tiers
Gemini 3.1 Pro: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-09-26; no Gemini 3.5 Pro or Gemini 4 release yet.
Grok 4.7: $2/$6 per MTok (<200K prompt); $4/$12 for all tokens at ≥200K. Cached input $0.50/MTok ($1.00 at ≥200K). US regional endpoint 1.1x. No Batch API; the 2x-price 'Fast' variant is Cursor and Grok Build only. xAI lists no separate text-output cap. Knowledge cutoff May 2026.
Gemini 3.1 Pro
Gemini 3.1 Pro is Google's only Pro model (still in Preview), with a 1M-token context and strong multimodal reasoning.
Best for
- Very long documents
- Multimodal reasoning
- Frontier knowledge work
Watch out
Still in Preview; Google says Gemini 4 is in post-training but unreleased as of 2026-09-26, and newer Flash models outscore it on the Artificial Analysis index.
Grok 4.7
Grok 4.7 is xAI's flagship since 2026-09-21 — a larger new base model with longer RL on multi-hour tasks, at Grok 4.6's $2/$6 price.
Best for
- Coding agents
- Cost-sensitive frontier work
- Cursor and GitHub Copilot users
Watch out
Token rates double above a 200K-token prompt and there is no Batch API; the DeepSWE figure is vendor-reported.
When the cheaper one wins
Grok 4.7 is cheaper on output at $6 per million tokens against $12 for Gemini 3.1 Pro — about 2.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 3/5 core specs verified on both sides — Not published for at least one side: max output, parameter count.
- 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
- Verified within the last 90 days — Newest catalog check was 2 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- Gemini 3.1 Pro: Google Cloud — Gemini 3.1 Pro (accessed 2026-08-29)
- Gemini 3.1 Pro: TechCrunch — Gemini 3.1 Pro (accessed 2026-08-29)
- Grok 4.7: xAI — Grok 4.7 (accessed 2026-09-26)
- Grok 4.7: xAI docs — grok-4.7 model card (accessed 2026-09-26)
- Grok 4.7: xAI pricing (grok-4.7 $2/$6, ≥200K $4/$12) (accessed 2026-09-26)
- Grok 4.7: GitHub changelog — Grok 4.7 in Copilot (2026-09-21) (accessed 2026-09-26)
Related comparisons
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- Claude Fable 5.1 vs Gemini 3.1 Pro
- Claude Fable 5.1 vs Grok 4.7
Diving deeper on one model? Gemini 3.1 Pro · Grok 4.7
Common questions
Gemini 3.1 Pro vs Grok 4.7
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.1 Pro or Grok 4.7 cheaper for input?
Is Gemini 3.1 Pro or Grok 4.7 cheaper for output?
Which has the larger context window, Gemini 3.1 Pro or Grok 4.7?
Do Gemini 3.1 Pro and Grok 4.7 support the same reasoning levels?
Should I use Gemini 3.1 Pro or Grok 4.7?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Context: Gemini 3.1 Pro takes 1.05M against 500K for Grok 4.7 — only decisive if your prompts approach the smaller window.
- Measured capability: Grok 4.7 leads Artificial Analysis Intelligence Index 46.4 to 29.7 (measured 2026-09-26).
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.