Agentic
Like-for-like- Grok 4.3
- 26.9
- Supported · #144/152
- Muse Spark
- 56.1
- Supported · #34/152
- Basis
- BenchAlign lane · 3 vs 5 public rows
- Reading
- Muse Spark leads · intervals overlap
Grok 4.3 vs Muse Spark
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
Muse Spark
Muse Spark leads on the public coding lane, 59 to 33.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark
Muse Spark leads on the public agentic lane, 56.1 to 26.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Grok 4.3
Grok 4.3 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Grok 4.3 only
24 Muse Spark only
- Shared results
- 0
- Grok 4.3 only
- 7
- Muse Spark only
- 24
- Like-for-like categories
- 3 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Like-for-like- Grok 4.3
- 33.6
- Supported · #131/151
- Muse Spark
- 59.0
- Supported · #25/151
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- Muse Spark leads
Knowledge
Like-for-like- Grok 4.3
- 57.0
- Supported · #44/183
- Muse Spark
- 65.4
- Supported · #24/183
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Muse Spark leads · intervals overlap
Multimodal
Directional only- Grok 4.3
- 65.6
- #25/48
- Muse Spark
- 77.5
- #14/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Directional only
Instruction following
Directional only- Grok 4.3
- 94.4
- #2/123
- Muse Spark
- 93.2
- #8/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Grok 4.3
- 65.2
- Unranked · 2 rankable rows
- Muse Spark
- 45.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Grok 4.3
- Not ranked
- Muse Spark
- 55.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Grok 4.3
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Grok 4.3 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 26.9Supported · #144/152 | 56.1Supported · #34/152 | Like-for-likeBenchAlign lane · 3 vs 5 public rows | Muse Spark leads · intervals overlap |
| Coding | 33.6Supported · #131/151 | 59.0Supported · #25/151 | Like-for-likeBenchAlign lane · 2 vs 4 public rows | Muse Spark leads |
| Knowledge | 57.0Supported · #44/183 | 65.4Supported · #24/183 | Like-for-likeBenchAlign lane · 2 vs 5 public rows | Muse Spark leads · intervals overlap |
| Multimodal | 65.6#25/48 | 77.5#14/48 | Directional onlyProvisional lane · 0 vs 2 weighted rows | Directional only |
| Instruction following | 94.4#2/123 | 93.2#8/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 65.2Unranked · 2 rankable rows | 45.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 55.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
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 4.3
- $0.0025
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Grok 4.3
- $0.07
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Grok 4.3
- $0.09
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Muse Spark has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGrok 4.3Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Grok 4.3
1M
Muse Spark
262K
API model ID
Grok 4.3
Not sourced
Muse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Grok 4.3
$0.2 per 1M cached input tokens
Muse Spark
No comparable hosted API rate
Documented inputs
Grok 4.3
Not sourced
Muse Spark
Not sourced
Documented outputs
Grok 4.3
Not sourced
Muse Spark
Not sourced
Provider availability
Grok 4.3
Not sourced
Muse Spark
Not sourced
Reasoning profile
Grok 4.3
Reasoning
Muse Spark
Reasoning
Weight access
Grok 4.3
Proprietary
Muse Spark
Proprietary
License
Grok 4.3
Proprietary
Muse Spark
Proprietary
Release date
Grok 4.3
2026-04-30
Muse Spark
2026-04-08
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Grok 4.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence31 rows
Agentic
Gert Labs
Grok 4.343.86%SourceMuse Spark—Not directly comparable
ResearchClawBench
Grok 4.312.4%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.1 (Vals)
Grok 4.341.9%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Grok 4.3—Muse Spark59%SourceNot directly comparable
τ²-bench results
Grok 4.3—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Grok 4.3—Muse Spark74.8%SourceNot directly comparable
CyberGym
Grok 4.3—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Grok 4.3—Muse Spark63.8%SourceNot directly comparable
Coding
LiveCodeBench (Vals)
Grok 4.384.5%SourceMuse Spark—Not directly comparable
SWE-bench (Vals)
Grok 4.371.4%SourceMuse Spark—Not directly comparable
SWE-bench Verified
Grok 4.3—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Grok 4.3—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Grok 4.3—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Grok 4.3—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
Grok 4.3—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Grok 4.391.4%SourceMuse Spark—Not directly comparable
MMLU-Pro (Vals)
Grok 4.385.8%SourceMuse Spark—Not directly comparable
GPQA-D
Grok 4.3—Muse Spark89.5%SourceNot directly comparable
HLE
Grok 4.3—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Grok 4.3—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Grok 4.3—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Grok 4.3—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
Grok 4.3—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Grok 4.3—Muse Spark80.4%SourceNot directly comparable
ERQA
Grok 4.3—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Grok 4.3—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Grok 4.3—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Grok 4.3—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Grok 4.3—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, Grok 4.3 or Muse Spark?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Grok 4.3 or Muse Spark?
Muse Spark leads the public coding lane, 59 to 33.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Grok 4.3 or Muse Spark?
Muse Spark leads the public agentic tasks lane, 56.1 to 26.9, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Grok 4.3 or Muse Spark?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Grok 4.3 or Muse Spark?
Grok 4.3 has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 10, 2026
Watch Grok 4.3 vs Muse Spark
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.