Long documents
Prompts that approach the documented context limit
Muse Spark 1.3
Muse Spark 1.3 has the larger documented context window.
Confidence: documented
Mistral Medium 3.5 128B vs Muse Spark 1.3
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model A
Mistral
33.62/100
Estimated · Public rank #162
Conditional range 19.3–48.0
- Shared results
- 1
- Mistral Medium 3.5 128B only
- 6
- Muse Spark 1.3 only
- 19
- Like-for-like categories
- 0 / 8
Estimated: Mistral Medium 3.5 128B. 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.
Chat turn cost
1K fresh input + 500 output tokens
Muse Spark 1.3
Muse Spark 1.3 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 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
22.6Mistral Medium 3.5 128B—Muse Spark 1.3
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.
6 Mistral Medium 3.5 128B only
1 Shared
19 Muse Spark 1.3 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.
Agentic
Not comparable- Mistral Medium 3.5 128B
- 19.8
- Supported · #108/128
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- Mistral Medium 3.5 128B
- 22.6
- Estimated · #114/150
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Medium 3.5 128B
- 73.5
- Unranked · 2 rankable rows
- Muse Spark 1.3
- 82.9
- #6/28
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Medium 3.5 128B
- 60.1
- Unranked · 1 rankable row
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Medium 3.5 128B
- 31.8
- Supported · #148/184
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Medium 3.5 128B
- 84.0
- #52/148
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 19.8Supported · #108/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 9 public rows | Not comparable |
| Coding | 22.6Estimated · #114/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 6 public rows | Not comparable |
| Reasoning | 73.5Unranked · 2 rankable rows | 82.9#6/28 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 60.1Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 31.8Supported · #148/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 84.0#52/148 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 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 Medium 3.5 128B
- $0.00525
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
- Muse Spark 1.3
- $0.0975
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Mistral Medium 3.5 128B 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.
SpecificationMistral Medium 3.5 128BMuse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
Muse Spark 1.3
API model ID
Mistral Medium 3.5 128B
Not sourced
Muse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mistral Medium 3.5 128B
Not published
Muse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
Mistral Medium 3.5 128B
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
Mistral Medium 3.5 128B
Not sourced
Muse Spark 1.3
Provider availability
Mistral Medium 3.5 128B
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
Mistral Medium 3.5 128B
Reasoning
Muse Spark 1.3
Reasoning
Weight access
Mistral Medium 3.5 128B
Open Weight
Muse Spark 1.3
Proprietary
License
Mistral Medium 3.5 128B
Open Weight
Muse Spark 1.3
Proprietary
Release date
Mistral Medium 3.5 128B
2026-04-29
Muse Spark 1.3
2026-09-02
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0975 vs $0.07525. Cache-heavy agent loop: $0.405 vs $0.0975.
- Context tradeoff
- Muse Spark 1.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Medium 3.5 128B or Muse Spark 1.3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Mistral Medium 3.5 128B or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Mistral Medium 3.5 128B or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Mistral Medium 3.5 128B or Muse Spark 1.3?
For the stated presets, chat costs $0.00525 on Mistral Medium 3.5 128B and $0.00337 on Muse Spark 1.3; repository review costs $0.0975 and $0.07525; the cache-heavy agent loop costs $0.405 and $0.0975. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Mistral Medium 3.5 128B or Muse Spark 1.3?
Muse Spark 1.3 has the larger documented context window: 1M, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Mistral Medium 3.5 128B
API / mo$6,750
Self-host / mo$6,990
Break-even71M/day
Muse Spark 1.3
API / mo$4,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
τ³-bench results
Mistral Medium 3.5 128B91.4%SourceMuse Spark 1.3—Not directly comparable
Gert Labs
Mistral Medium 3.5 128B39.10%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1 (Vals)
Mistral Medium 3.5 128B39.0%SourceMuse Spark 1.372.3%SourceMuse Spark 1.3 leads this result
Terminal-Bench 2.1
Mistral Medium 3.5 128B—Muse Spark 1.388.8%SourceNot directly comparable
JobBench
Mistral Medium 3.5 128B—Muse Spark 1.364.9%SourceNot directly comparable
OSWorld 2.0
Mistral Medium 3.5 128B—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
Mistral Medium 3.5 128B—Muse Spark 1.389.4%SourceNot directly comparable
AutomationBench
Mistral Medium 3.5 128B—Muse Spark 1.349.4%SourceNot directly comparable
ApprenticeBench
Mistral Medium 3.5 128B—Muse Spark 1.319%SourceNot directly comparable
CWE-bench v1
Mistral Medium 3.5 128B—Muse Spark 1.355.0%SourceNot directly comparable
MazeBench
Mistral Medium 3.5 128B—Muse Spark 1.30%SourceNot directly comparable
Coding
SWE-bench Verified
Mistral Medium 3.5 128B77.6%SourceMuse Spark 1.3—Not directly comparable
SWE-bench (Vals)
Mistral Medium 3.5 128B66.4%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
Mistral Medium 3.5 128B—Muse Spark 1.388.8%SourceNot directly comparable
DeepSWE
Mistral Medium 3.5 128B—Muse Spark 1.375.4%SourceNot directly comparable
SWE-Atlas Codebase QnA
Mistral Medium 3.5 128B—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
Mistral Medium 3.5 128B—Muse Spark 1.341.6%SourceNot directly comparable
Bug Hunt Bench
Mistral Medium 3.5 128B—Muse Spark 1.332.2 fixesSourceNot directly comparable
FrontierSWE v2
Mistral Medium 3.5 128B—Muse Spark 1.325.8%SourceNot directly comparable
Reasoning
Knowledge
Instruction following
Gray Swan IPI (15 attempts)
Mistral Medium 3.5 128B—Muse Spark 1.315.9%SourceNot directly comparable
Math
26 public results · 1 shared
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Last updated October 17, 2026