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.
Grok 4.6 has the higher public score, 66.47 versus 47.73, and the 90% score intervals do not overlap. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Grok 4.6: $2 input / $6 output
MiniMax M2.7: $0.30 input / $1.20 output
Grok 4.6: 500K
MiniMax M2.7: 200K
Grok 4.6: August 12, 2026
MiniMax M2.7: March 18, 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 35, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Grok 4.6
Grok 4.6 has the higher public agentic point estimate, 69.7 to 33, 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.
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
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.
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 9.0LiveCodeBench (Vals)Coding
Normalized gap 8.3Each 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 | Grok 4.6 | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 69.7Supported · #11/132 | 33.0Supported · #97/132 | Like-for-likeBenchAlign v5.8 lane · 7 vs 8 public rows | Grok 4.6 leads |
| Coding | 58.6Supported · #23/158 | 35.0Supported · #88/158 | Like-for-likeBenchAlign v5.8 lane · 9 vs 12 public rows | Grok 4.6 leads |
| Knowledge | 67.9Supported · #21/179 | 45.4Supported · #96/179 | Like-for-likeBenchAlign v5.8 lane · 2 vs 5 public rows | Grok 4.6 leads |
| Reasoning | 65.0Supported · #22/158 | 38.8Estimated · #87/158 | Directional onlyBenchAlign v5.8 lane · 3 vs 0 public rows | Directional only |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.0#14/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 65.6Unranked · 2 rankable rows | 46.6Unranked · 4 rankable rows | Not comparableProvisional lane · 2 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
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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.
Grok 4.6
MiniMax M2.7
200K
Grok 4.6
grok-4.6
xAI Grok 4.6 release notesMiniMax M2.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Grok 4.6
$0.5 per 1M cached input tokens
xAI Grok 4.6 release notesMiniMax M2.7
Not published
Grok 4.6
text, image
xAI model page for grok-4.6MiniMax M2.7
Grok 4.6
MiniMax M2.7
Grok 4.6
Not sourced
MiniMax M2.7
Not sourced
Grok 4.6
Reasoning
MiniMax M2.7
Non-Reasoning
Grok 4.6
Proprietary
MiniMax M2.7
Open Weight
Grok 4.6
Proprietary
MiniMax M2.7
Open Weight
Grok 4.6
2026-08-12
MiniMax M2.7
2026-03-18
Grok 4.6 has the higher public score, 66.47 versus 47.73, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Grok 4.6 has the higher public coding point estimate, 58.6 to 35, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Grok 4.6 has the higher public agentic tasks point estimate, 69.7 to 33, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.005 on Grok 4.6 and $0.0009 on MiniMax M2.7; repository review costs $0.118 and $0.0186; the cache-heavy agent loop costs $0.2 and $0.078. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Grok 4.6 has the larger documented context window: 500K, compared with 200K.
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)
Grok 4.6 leads this result
ApprenticeBench
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
τ³-bench results
Not directly comparable
Toolathlon
Not directly comparable
MLE-Bench Lite
Not directly comparable
MM-ClawBench
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
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)
Grok 4.6 leads this result
SWE-bench (Vals)
Grok 4.6 leads this result
CursorBench 4.0
Not directly comparable
SWE-bench Verified*
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE-Rebench
Not directly comparable
SWE Multilingual
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
GPQA Diamond (Vals)
Grok 4.6 leads this result
MMLU-Pro (Vals)
Grok 4.6 leads this result
HLE
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
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Last updated November 10, 2026
