Coding work
Code generation, repair, and software-engineering tasks
Grok 4.6
Grok 4.6 leads on the public coding lane, 62 to 51.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Opus 4.6 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 63.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Claude Opus 4.6 only
- 33
- Grok 4.6 only
- 16
- Like-for-like categories
- 2 / 8
Supported: Claude Opus 4.6 and 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 44.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: strongerLong documents
Prompts that approach the documented context limit
Claude Opus 4.6
Claude Opus 4.6 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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Grok 4.6
Grok 4.6 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallbackRepository 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-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.
51.1Claude Opus 4.662.0Grok 4.6
Like-for-like · BenchAlign v5.7
Grok 4.6 leads the like-for-like coding row, although the 90% intervals overlap.
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.
33 Claude Opus 4.6 only
1 Shared
16 Grok 4.6 only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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- Claude Opus 4.6
- 44.3
- Supported · #39/105
- Grok 4.6
- 67.9
- Supported · #8/105
- Basis
- BenchAlign v5.7 lane · 10 vs 4 public rows
- Reading
- Grok 4.6 leads
Coding
Like-for-like- Claude Opus 4.6
- 51.1
- Supported · #36/135
- Grok 4.6
- 62.0
- Supported · #14/135
- Basis
- BenchAlign v5.7 lane · 8 vs 8 public rows
- Reading
- Grok 4.6 leads · intervals overlap
Knowledge
Directional only- Claude Opus 4.6
- 58.7
- Estimated · #37/158
- Grok 4.6
- 69.0
- Supported · #13/158
- Basis
- BenchAlign v5.7 lane · 9 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.6
- 68.1
- Unranked · 2 rankable rows
- Grok 4.6
- 57.6
- #16/19
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.6
- 59.6
- #30/50
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.6
- Not ranked
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.6
- 51.0
- #79/124
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.6
- 58.5
- Unranked · 3 rankable rows
- Grok 4.6
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.6 | Grok 4.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.3Supported · #39/105 | 67.9Supported · #8/105 | Like-for-likeBenchAlign v5.7 lane · 10 vs 4 public rows | Grok 4.6 leads |
| Coding | 51.1Supported · #36/135 | 62.0Supported · #14/135 | Like-for-likeBenchAlign v5.7 lane · 8 vs 8 public rows | Grok 4.6 leads · intervals overlap |
| Knowledge | 58.7Estimated · #37/158 | 69.0Supported · #13/158 | Directional onlyBenchAlign v5.7 lane · 9 vs 2 public rows | Directional only |
| Reasoning | 68.1Unranked · 2 rankable rows | 57.6#16/19 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | 59.6#30/50 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 51.0#79/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 58.5Unranked · 3 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
- Claude Opus 4.6
- $0.0175
- 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
- Claude Opus 4.6
- $0.325
- 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
- Claude Opus 4.6
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- Grok 4.6
- $0.2
- Fits in one request
Grok 4.6 has the lower modeled cost
Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
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.
SpecificationClaude Opus 4.6Grok 4.6
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
Grok 4.6
API model ID
Claude Opus 4.6
Not sourced
Grok 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.
Claude Opus 4.6
Not published
Grok 4.6
$0.5 per 1M cached input tokens
xAI Grok 4.6 release notesDocumented inputs
Claude Opus 4.6
Not sourced
Grok 4.6
Not sourced
Documented outputs
Claude Opus 4.6
Not sourced
Grok 4.6
Not sourced
Provider availability
Claude Opus 4.6
Not sourced
Grok 4.6
Not sourced
Reasoning profile
Claude Opus 4.6
Non-Reasoning
Grok 4.6
Reasoning
Weight access
Claude Opus 4.6
Proprietary
Grok 4.6
Proprietary
License
Claude Opus 4.6
Proprietary
Grok 4.6
Proprietary
Release date
Claude Opus 4.6
2026-02-01
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 63.64, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.118. Cache-heavy agent loop: $1.35 vs $0.2.
- Context tradeoff
- Claude Opus 4.6 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.6 or Grok 4.6?
Grok 4.6 has the higher public score estimate, 69.19 versus 63.64, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.6 or Grok 4.6?
Grok 4.6 leads the public coding lane, 62 to 51.1, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Opus 4.6 or Grok 4.6?
Grok 4.6 leads the public agentic tasks lane, 67.9 to 44.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Claude Opus 4.6 or Grok 4.6?
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.005 on Grok 4.6; repository review costs $0.325 and $0.118; the cache-heavy agent loop costs $1.35 and $0.2. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.6 or Grok 4.6?
Claude Opus 4.6 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 evidence50 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.665.4%SourceGrok 4.6—Not directly comparable
BrowseComp
Claude Opus 4.683.7%SourceGrok 4.6—Not directly comparable
OSWorld-Verified
Claude Opus 4.672.7%SourceGrok 4.6—Not directly comparable
Claw-Eval
Claude Opus 4.670.4%SourceGrok 4.6—Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%SourceGrok 4.6—Not directly comparable
CyberGym
Claude Opus 4.666.6%SourceGrok 4.6—Not directly comparable
Gert Labs
Claude Opus 4.661.85%SourceGrok 4.6—Not directly comparable
ResearchClawBench
Claude Opus 4.619.9%SourceGrok 4.6—Not directly comparable
JobBench
Claude Opus 4.636.7%SourceGrok 4.6—Not directly comparable
ApprenticeBench
Shared sourceClaude Opus 4.65%Grok 4.613%Grok 4.6 leads this result
Terminal-Bench 3.0
Claude Opus 4.6—Grok 4.626.5%SourceNot directly comparable
APEX-Agents
Claude Opus 4.6—Grok 4.657.5%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—Grok 4.678.3%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.680.8%SourceGrok 4.6—Not directly comparable
SWE-bench Verified*
Claude Opus 4.675.6%SourceGrok 4.6—Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%SourceGrok 4.6—Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%SourceGrok 4.6—Not directly comparable
SWE-Rebench
Claude Opus 4.665.3%SourceGrok 4.6—Not directly comparable
React Native Evals
Claude Opus 4.684.1%SourceGrok 4.6—Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%SourceGrok 4.6—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%SourceGrok 4.6—Not directly comparable
Bug Hunt Bench
Claude Opus 4.6—Grok 4.627 fixesSourceNot directly comparable
DeepSWE
Claude Opus 4.6—Grok 4.665.9%SourceNot directly comparable
cursorBench32
Claude Opus 4.6—Grok 4.670.8%SourceNot directly comparable
FrontierCode 1.1 Extended
Claude Opus 4.6—Grok 4.661.3%SourceNot directly comparable
VulcanBench v3
Claude Opus 4.6—Grok 4.687.0%SourceNot directly comparable
FrontierSWE v2
Claude Opus 4.6—Grok 4.625.3%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6—Grok 4.688.2%SourceNot directly comparable
SWE-bench (Vals)
Claude Opus 4.6—Grok 4.695.6%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
Claude Opus 4.691.3%SourceGrok 4.6—Not directly comparable
GPQA-D
Claude Opus 4.689.2%SourceGrok 4.6—Not directly comparable
SuperGPQA
Claude Opus 4.695%SourceGrok 4.6—Not directly comparable
MMLU-Pro
Claude Opus 4.682%SourceGrok 4.6—Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%SourceGrok 4.6—Not directly comparable
HLE
Claude Opus 4.653%SourceGrok 4.6—Not directly comparable
HLE w/o tools
Claude Opus 4.640%SourceGrok 4.6—Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%SourceGrok 4.6—Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%SourceGrok 4.6—Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—Grok 4.694.7%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—Grok 4.689.4%SourceNot directly comparable
Math
50 public results · 1 shared
Watch Claude Opus 4.6 vs Grok 4.6
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated September 25, 2026