Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Large 4 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Mistral Large 4 vs Muse Spark 1.1
Updated October 6, 2026. Rank says Muse Spark 1.1 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
Muse Spark 1.1 has the higher public point estimate, 65.94 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Mistral Large 4 only
- 1
- Muse Spark 1.1 only
- 24
- Like-for-like categories
- 1 / 8
Estimated: Mistral Large 4 · Supported: Muse Spark 1.1. Conditional ranges do not establish rank confidence.How 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mistral Large 4 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-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-ratesRepository 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
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.
—Mistral Large 454.1Muse Spark 1.1
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
1 Mistral Large 4 only
2 Shared
24 Muse Spark 1.1 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.
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 v5.8 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.
Knowledge
Like-for-like- Mistral Large 4
- 55.1
- Supported · #51/173
- Muse Spark 1.1
- 66.7
- Supported · #21/173
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Muse Spark 1.1 leads · intervals overlap
Agentic
Not comparable- Mistral Large 4
- Not ranked
- Muse Spark 1.1
- 56.8
- Supported · #31/120
- Basis
- BenchAlign v5.8 lane · 2 vs 14 public rows
- Reading
- Not comparable
Coding
Not comparable- Mistral Large 4
- Not ranked
- Muse Spark 1.1
- 54.1
- Supported · #33/144
- Basis
- BenchAlign v5.8 lane · 1 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Large 4
- 78.2
- Unranked · 2 rankable rows
- Muse Spark 1.1
- 75.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Large 4
- 73.6
- Unranked · 1 rankable row
- Muse Spark 1.1
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Large 4
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Large 4
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Large 4
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Large 4 | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 55.1Supported · #51/173 | 66.7Supported · #21/173 | Like-for-likeBenchAlign v5.8 lane · 0 vs 5 public rows | Muse Spark 1.1 leads · intervals overlap |
| Agentic | Not ranked | 56.8Supported · #31/120 | Not comparableBenchAlign v5.8 lane · 2 vs 14 public rows | Not comparable |
| Coding | Not ranked | 54.1Supported · #33/144 | Not comparableBenchAlign v5.8 lane · 1 vs 4 public rows | Not comparable |
| Reasoning | 78.2Unranked · 2 rankable rows | 75.7Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.6Unranked · 1 rankable row | 78.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 1 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 v5.8) 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.
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
- Mistral Large 4
- $0.00172
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Mistral Large 4
- $0.04027
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Mistral Large 4
- $0.0485
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Muse Spark 1.1 has no comparable published API token 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.
SpecificationMistral Large 4Muse Spark 1.1
Context window
Maximum documented context; output-token limits may be lower.
Mistral Large 4
Muse Spark 1.1
1M
API model ID
Mistral Large 4
Not sourced
Muse Spark 1.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mistral Large 4
$0.07 per 1M cached input tokens
Mistral AI Mistral Large 4 model documentationMuse Spark 1.1
No comparable hosted API rate
Documented inputs
Mistral Large 4
Not sourced
Muse Spark 1.1
Not sourced
Documented outputs
Mistral Large 4
Not sourced
Muse Spark 1.1
Not sourced
Provider availability
Mistral Large 4
Not sourced
Muse Spark 1.1
Not sourced
Reasoning profile
Mistral Large 4
Hybrid
Muse Spark 1.1
Reasoning
Weight access
Mistral Large 4
Pending
Muse Spark 1.1
Proprietary
License
Mistral Large 4
Pending
Muse Spark 1.1
Proprietary
Release date
Mistral Large 4
2026-10-06
Muse Spark 1.1
2026-07-09
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
- Muse Spark 1.1 has the higher public point estimate, 65.94 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Large 4 or Muse Spark 1.1?
Muse Spark 1.1 has the higher public point estimate, 65.94 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Mistral Large 4 or Muse Spark 1.1?
Mistral Large 4 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Mistral Large 4 or Muse Spark 1.1?
Mistral Large 4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Mistral Large 4 or Muse Spark 1.1?
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, Mistral Large 4 or Muse Spark 1.1?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence27 rows
Agentic
Cybench
Mistral Large 493.0%SourceMuse Spark 1.192.9%SourceMistral Large 4 leads this result
Finance Agent v2
Mistral Large 454.7%SourceMuse Spark 1.157.2%SourceMuse Spark 1.1 leads this result
Terminal-Bench 2.1
Mistral Large 4—Muse Spark 1.180.0%SourceNot directly comparable
MCP Atlas
Mistral Large 4—Muse Spark 1.188.1%SourceNot directly comparable
Toolathlon
Mistral Large 4—Muse Spark 1.175.6%SourceNot directly comparable
OSWorld-Verified
Mistral Large 4—Muse Spark 1.180.8%SourceNot directly comparable
WebArena-Verified
Mistral Large 4—Muse Spark 1.169%SourceNot directly comparable
DeepSearchQA
Mistral Large 4—Muse Spark 1.184.9%SourceNot directly comparable
CyberGym
Mistral Large 4—Muse Spark 1.159.0%SourceNot directly comparable
deepSwe
Mistral Large 4—Muse Spark 1.153.3%SourceNot directly comparable
OSWorld 2.0
Mistral Large 4—Muse Spark 1.114.2%SourceNot directly comparable
JobBench
Mistral Large 4—Muse Spark 1.154.7%SourceNot directly comparable
ExploitGym
Mistral Large 4—Muse Spark 1.10.8%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Mistral Large 4—Muse Spark 1.169.3%SourceNot directly comparable
Coding
Vibe Code Bench
Mistral Large 478.40%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1
Mistral Large 4—Muse Spark 1.180.0%SourceNot directly comparable
SWE-bench Pro
Mistral Large 4—Muse Spark 1.161.5%SourceNot directly comparable
LiveCodeBench (Vals)
Mistral Large 4—Muse Spark 1.185.9%SourceNot directly comparable
SWE-bench (Vals)
Mistral Large 4—Muse Spark 1.182.0%SourceNot directly comparable
Reasoning
MRCR 1M
Mistral Large 4—Muse Spark 1.154.1%SourceNot directly comparable
Multimodal
Knowledge
HLE
Mistral Large 4—Muse Spark 1.162.1%SourceNot directly comparable
HLE w/o tools
Mistral Large 4—Muse Spark 1.152.2%SourceNot directly comparable
HealthBench Professional
Mistral Large 4—Muse Spark 1.159.3%SourceNot directly comparable
GPQA Diamond (Vals)
Mistral Large 4—Muse Spark 1.191.2%SourceNot directly comparable
MMLU-Pro (Vals)
Mistral Large 4—Muse Spark 1.188.7%SourceNot directly comparable
27 public results · 2 shared
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Last updated October 6, 2026