Coding work
Code generation, repair, and software-engineering tasks
Grok 4.6
Grok 4.6 leads on the public coding lane, 62 to 56, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Grok 4.6 vs Muse Spark 1.2
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 64.99, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Grok 4.6 only
- 11
- Muse Spark 1.2 only
- 2
- Like-for-like categories
- 3 / 8
Supported: Grok 4.6 · Estimated: Muse Spark 1.2How 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 59.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Muse Spark 1.2
Muse Spark 1.2 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
Muse Spark 1.2
Muse Spark 1.2 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
Muse Spark 1.2
Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Muse Spark 1.2
Muse Spark 1.2 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.
62.0Grok 4.656.0Muse Spark 1.2
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.
11 Grok 4.6 only
6 Shared
2 Muse Spark 1.2 only
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.
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 1.1Grok 4.6:89.4%Muse Spark 1.2:88.3%
Bars run 0–100 on each benchmark’s normalized display scale
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- Grok 4.6
- 67.9
- Supported · #8/105
- Muse Spark 1.2
- 59.5
- Supported · #17/105
- Basis
- BenchAlign v5.7 lane · 4 vs 2 public rows
- Reading
- Grok 4.6 leads · intervals overlap
Coding
Like-for-like- Grok 4.6
- 62.0
- Supported · #14/135
- Muse Spark 1.2
- 56.0
- Supported · #25/135
- Basis
- BenchAlign v5.7 lane · 8 vs 5 public rows
- Reading
- Grok 4.6 leads · intervals overlap
Knowledge
Like-for-like- Grok 4.6
- 69.0
- Supported · #13/158
- Muse Spark 1.2
- 65.0
- Supported · #21/158
- Basis
- BenchAlign v5.7 lane · 2 vs 1 public rows
- Reading
- Grok 4.6 leads · intervals overlap
Reasoning
Not comparable- Grok 4.6
- 57.6
- #16/19
- Muse Spark 1.2
- 76.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Grok 4.6
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Grok 4.6
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Grok 4.6
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Grok 4.6
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Grok 4.6 | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 67.9Supported · #8/105 | 59.5Supported · #17/105 | Like-for-likeBenchAlign v5.7 lane · 4 vs 2 public rows | Grok 4.6 leads · intervals overlap |
| Coding | 62.0Supported · #14/135 | 56.0Supported · #25/135 | Like-for-likeBenchAlign v5.7 lane · 8 vs 5 public rows | Grok 4.6 leads · intervals overlap |
| Knowledge | 69.0Supported · #13/158 | 65.0Supported · #21/158 | Like-for-likeBenchAlign v5.7 lane · 2 vs 1 public rows | Grok 4.6 leads · intervals overlap |
| Reasoning | 57.6#16/19 | 76.4Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 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 |
| Math | 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 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
- Grok 4.6
- $0.005
- Fits in one request
- Muse Spark 1.2
- $0.00338
- Fits in one request
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Grok 4.6
- $0.118
- Fits in one request
- Muse Spark 1.2
- $0.07525
- Fits in one request
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Grok 4.6
- $0.2
- Fits in one request
- Muse Spark 1.2
- $0.0975
- Fits in one request
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationGrok 4.6Muse Spark 1.2
Context window
Maximum documented context; output-token limits may be lower.
Grok 4.6
Muse Spark 1.2
API model ID
Grok 4.6
grok-4.6
xAI Grok 4.6 release notesMuse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageCached-input rate
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 notesMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageDocumented inputs
Grok 4.6
Not sourced
Muse Spark 1.2
Not sourced
Documented outputs
Grok 4.6
Not sourced
Muse Spark 1.2
Not sourced
Provider availability
Grok 4.6
Not sourced
Muse Spark 1.2
Not sourced
Reasoning profile
Grok 4.6
Reasoning
Muse Spark 1.2
Reasoning
Weight access
Grok 4.6
Proprietary
Muse Spark 1.2
Proprietary
License
Grok 4.6
Proprietary
Muse Spark 1.2
Proprietary
Release date
Grok 4.6
2026-08-12
Muse Spark 1.2
2026-08-05
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 64.99, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.118 vs $0.07525. Cache-heavy agent loop: $0.2 vs $0.0975.
- Context tradeoff
- Muse Spark 1.2 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Grok 4.6 or Muse Spark 1.2?
Grok 4.6 has the higher public score estimate, 69.19 versus 64.99, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Grok 4.6 or Muse Spark 1.2?
Grok 4.6 leads the public coding lane, 62 to 56, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Grok 4.6 or Muse Spark 1.2?
Grok 4.6 leads the public agentic tasks lane, 67.9 to 59.5, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Grok 4.6 or Muse Spark 1.2?
For the stated presets, chat costs $0.005 on Grok 4.6 and $0.00337 on Muse Spark 1.2; repository review costs $0.118 and $0.07525; the cache-heavy agent loop costs $0.2 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, Grok 4.6 or Muse Spark 1.2?
Muse Spark 1.2 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 evidence19 rows
Agentic
Terminal-Bench 3.0
Grok 4.626.5%SourceMuse Spark 1.2—Not directly comparable
APEX-Agents
Grok 4.657.5%SourceMuse Spark 1.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
Grok 4.678.3%SourceMuse Spark 1.269.7%SourceGrok 4.6 leads this result
ApprenticeBench
Grok 4.613%SourceMuse Spark 1.2—Not directly comparable
Terminal-Bench 2.1
Grok 4.6—Muse Spark 1.282.9%SourceNot directly comparable
Coding
Bug Hunt Bench
Grok 4.627 fixesSourceMuse Spark 1.2—Not directly comparable
DeepSWE
Grok 4.665.9%SourceMuse Spark 1.259.3%SourceGrok 4.6 leads this result
cursorBench32
Grok 4.670.8%SourceMuse Spark 1.2—Not directly comparable
FrontierCode 1.1 Extended
Grok 4.661.3%SourceMuse Spark 1.2—Not directly comparable
VulcanBench v3
Grok 4.687.0%SourceMuse Spark 1.287.0%SourceTie
FrontierSWE v2
Shared sourceGrok 4.625.3%Muse Spark 1.212.0%Grok 4.6 leads this result
LiveCodeBench (Vals)
Grok 4.688.2%SourceMuse Spark 1.2—Not directly comparable
SWE-bench (Vals)
Grok 4.695.6%SourceMuse Spark 1.286.6%SourceGrok 4.6 leads this result
Terminal-Bench 2.1
Grok 4.6—Muse Spark 1.282.9%SourceNot directly comparable
Reasoning
Knowledge
19 public results · 6 shared
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Last updated September 25, 2026