Grok 4.20 vs Kimi K3.1 Pro
Kimi K3.1 Pro leads Grok 4.20 on coding, 82.2 to 33.3 on the Vibe Coding Index, and Grok 4.20 costs 4.0x less per output token ($2.50 vs $10.00 per million). Updated October 29, 2026; 12 benchmarks have a result for both.
Grok 4.20 or Kimi K3.1 Pro: which to pick
Pick Kimi K3.1 Pro for coding: it scores 82.2 on the Vibe Coding Index against 33.3 for Grok 4.20.
Pick Grok 4.20 for output tokens 4.0x cheaper ($2.50 vs $10.00 per million) and a larger context window (2M against 1.05M tokens).
- Shared results
- 12
- Grok 4.20 only
- 41
- Kimi K3.1 Pro only
- 23
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
Kimi K3.1 Pro
Kimi K3.1 Pro scores 82.2 against 33.3.
2 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only Kimi K3.1 Pro has a AA Intelligence Index result, 46.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Grok 4.20 has a GPQA Diamond result, 88.6%.
Directional only
Math
AIME
No call
Only Grok 4.20 has a AIME result, 96.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Grok 4.20 has a MMLU Pro result, 86.3%.
Directional only
Web design
Design Arena: websites
Kimi K3.1 Pro
Kimi K3.1 Pro leads, 1325 against 1228.
Same board, both measured
General
Vals Index
No call
Only Kimi K3.1 Pro has a Vals Index result, 62.5%.
Directional only
Long documents
Listed context window
Grok 4.20
Grok 4.20 takes 2M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Grok 4.20
Grok 4.20 $0.0025, Kimi K3.1 Pro $0.0070 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Grok 4.20 has the number, $0.72.
Directional only
At a glance
- Coding index
- Grok 4.20 33.3 from 2 benchmarks, Kimi K3.1 Pro 82.2 from 4 benchmarks.
- Widest category
- Coding, 80.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): Grok 4.20 4.1% against Kimi K3.1 Pro 84.9%, 80.8 points apart on a 0 to 100 scale.
- Output price
- Grok 4.20 $2.50 against Kimi K3.1 Pro $10.00 per million output tokens, 4.0x apart.
- Context window
- Grok 4.20 takes 2M tokens, Kimi K3.1 Pro 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Grok 4.20 | Bars | Kimi K3.1 Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 1 | ||
| Web design | 0 | 7 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 81.8% | Only Kimi K3.1 Pro measured |
| LMArena WebDev | Not measured | 1695 | Only Kimi K3.1 Pro measured |
| LiveCodeBench (Vals AI) | 84.3% | Not measured | Only Grok 4.20 measured |
| SWE-bench Verified (Vals AI) | 72.2% | Not measured | Only Grok 4.20 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 63.6% | Only Kimi K3.1 Pro measured |
| Vibe Code Bench (Vals AI) | 4.1% | 84.9% | Kimi K3.1 Pro |
| Code migration (Vals AI) | 0.3% | Not measured | Only Grok 4.20 measured |
| IOI (Vals AI) | Not measured | 81.9% | Only Kimi K3.1 Pro measured |
| Terminal-Bench 2.1 (Vals AI) | 44.2% | Not measured | Only Grok 4.20 measured |
| SciCode | Not measured | 60.8% | Only Kimi K3.1 Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | Not measured | Only Grok 4.20 measured |
| Terminal-Bench | 57.3% | Not measured | Only Grok 4.20 measured |
| WeirdML | 52.3% | Not measured | Only Grok 4.20 measured |
| ALE-Bench | Not measured | 1932.8 | Only Kimi K3.1 Pro measured |
| LMArena WebDev: React | Not measured | 1701 | Only Kimi K3.1 Pro measured |
| LMArena WebDev: HTML | Not measured | 1706 | Only Kimi K3.1 Pro measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 46.0 | Only Kimi K3.1 Pro measured |
| Epoch Capabilities Index | 152.1 | Not measured | Only Grok 4.20 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1144 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building full-stack apps | 1054 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building Android apps | 1043 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building Godot games | 1089 | Not measured | Only Grok 4.20 measured |
| Design Arena: HTML slides | 1180 | Not measured | Only Grok 4.20 measured |
| LiveBench research | Not measured | 77.9% | Only Kimi K3.1 Pro measured |
| LiveBench browser | Not measured | 75.9% | Only Kimi K3.1 Pro measured |
| LiveBench long agentic | Not measured | 39.1% | Only Kimi K3.1 Pro measured |
| Finance Agent (Vals AI) | 28.5% | Not measured | Only Grok 4.20 measured |
| Tax Agent Bench (Vals AI) | 7.0% | 24.9% | Kimi K3.1 Pro |
| Legal Research Bench (Vals AI) | 13.9% | 37.5% | Kimi K3.1 Pro |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 11.7% | Only Kimi K3.1 Pro measured |
| Excel Modeling Benchmark (Vals AI) | 11.7% | Not measured | Only Grok 4.20 measured |
| BioMysteryBench (Vals AI) | Not measured | 75.6% | Only Kimi K3.1 Pro measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 31.4% | Only Kimi K3.1 Pro measured |
| Vals Science Index | Not measured | 44.4% | Only Kimi K3.1 Pro measured |
| τ²-Bench Telecom | 93.0% | Not measured | Only Grok 4.20 measured |
| Vending-Bench 2 | $4,663 | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 88.6% | Not measured | Only Grok 4.20 measured |
| LiveBench reasoning | Not measured | 91.1% | Only Kimi K3.1 Pro measured |
| MysteryMechanism (Vals AI) | Not measured | 26.1% | Only Kimi K3.1 Pro measured |
| MMMU Pro (Vals AI) | 83.5% | Not measured | Only Grok 4.20 measured |
| GPQA Diamond (Artificial Analysis run) | 91.1% | Not measured | Only Grok 4.20 measured |
| Humanity's Last Exam (Artificial Analysis run) | 34.5% | 52.5% | Kimi K3.1 Pro |
| ARC-AGI | 89.5% | Not measured | Only Grok 4.20 measured |
| ARC-AGI-2 | 65.1% | Not measured | Only Grok 4.20 measured |
| Kagi LLM Benchmark | 75.0% | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| AIME | 96.5% | Not measured | Only Grok 4.20 measured |
| ProofBench | Not measured | 91.0% | Only Kimi K3.1 Pro measured |
| MathArena | Not measured | 59.6% | Only Kimi K3.1 Pro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.3% | Not measured | Only Grok 4.20 measured |
| LegalBench (Vals AI) | 77.7% | Not measured | Only Grok 4.20 measured |
| CaseLaw (Vals AI) | 54.4% | Not measured | Only Grok 4.20 measured |
| CorpFin (Vals AI) | 63.7% | Not measured | Only Grok 4.20 measured |
| TaxEval (Vals AI) | 74.1% | Not measured | Only Grok 4.20 measured |
| MortgageTax (Vals AI) | 45.4% | Not measured | Only Grok 4.20 measured |
| MedQA (Vals AI) | 94.5% | Not measured | Only Grok 4.20 measured |
| MedCode (Vals AI) | 32.2% | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1228 | 1325 | Kimi K3.1 Pro |
| Design Arena: UI components | 1194 | 1400 | Kimi K3.1 Pro |
| Design Arena: game dev | 1191 | 1410 | Kimi K3.1 Pro |
| Design Arena: data viz | 1217 | 1300 | Kimi K3.1 Pro |
| Design Arena: 3D | 1190 | 1451 | Kimi K3.1 Pro |
| Design Arena: all code categories | 1219 | 1354 | Kimi K3.1 Pro |
| Design Arena: SVG | 1161 | 1328 | Kimi K3.1 Pro |
| Design Arena: ASCII art | 1172 | Not measured | Only Grok 4.20 measured |
| LiveBench design | Not measured | 77.8% | Only Kimi K3.1 Pro measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 62.5% | Only Kimi K3.1 Pro measured |
| LiveBench data analysis | Not measured | 79.1% | Only Kimi K3.1 Pro measured |
| MedScribe (Vals AI) | 63.4% | Not measured | Only Grok 4.20 measured |
| Vals Multimodal Index | 39.1% | Not measured | Only Grok 4.20 measured |
| IFBench | 81.2% | Not measured | Only Grok 4.20 measured |
| Long context reasoning (AA-LCR) | 69.0% | 86.3% | Kimi K3.1 Pro |
| DTBench | 90.1% | Not measured | Only Grok 4.20 measured |
| LMCA | 38.7% | Not measured | Only Grok 4.20 measured |
| CL-bench | 22.2% | Not measured | Only Grok 4.20 measured |
| CL-bench Life | 11.9% | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 38 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok 4.20 | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Grok 4.20 costs less
Repository review
50K input + 3K output tokens
Grok 4.20 costs less
Agent loop
200K input + 10K output tokens
Grok 4.20 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | SpaceXAI Grok 4.20 | MoonshotAI Kimi K3.1 Pro |
|---|---|---|
| Maker | xAI | Moonshot AI |
| Weights | No public weights listed | No public weights listed |
| Released | March 31, 2026 | October 23, 2026 |
| Context window | 2M | 1.05M |
| Input $/Mtok | $1.25 | $2.00 |
| Output $/Mtok | $2.50 | $10.00 |
| Coding index | 33.3 | 82.2 |
| Independent measurements behind it | 2 | 4 |
| Cost per solved task | $0.72 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Grok 4.20 better than Kimi K3.1 Pro for coding?
Pick Kimi K3.1 Pro for coding: it scores 82.2 on the Vibe Coding Index against 33.3 for Grok 4.20. Pick Grok 4.20 for output tokens 4.0x cheaper ($2.50 vs $10.00 per million) and a larger context window (2M against 1.05M tokens).
Which is cheaper, Grok 4.20 or Kimi K3.1 Pro?
Grok 4.20 costs $1.25 per million input tokens and $2.50 per million output tokens; Kimi K3.1 Pro costs $2.00 and $10.00. Cost per solved task is known for Grok 4.20 only.
Which has the bigger context window, Grok 4.20 or Kimi K3.1 Pro?
Grok 4.20, with 2M tokens against 1.05M.
How many benchmarks is the Grok 4.20 vs Kimi K3.1 Pro comparison based on?
12 benchmarks have a result for both models as of October 29, 2026. Grok 4.20's coding index rests on 2 independent benchmarks and Kimi K3.1 Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.