Long documents
Prompts that approach the documented context limit
SubQ Snap 1.1
SubQ Snap 1.1 has the larger documented context window.
Confidence: documented
Mistral Medium 3.5 128B vs SubQ Snap 1.1
Updated November 11, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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.
Model A
Mistral
29.7/100
Estimated · Public rank #179
Conditional range 15.3–44.0
- API price per 1M tokens
Mistral Medium 3.5 128B: $1.50 input / $7.50 output
SubQ Snap 1.1: $0.040 input / $0 output
- Context window
Mistral Medium 3.5 128B: 256K
SubQ Snap 1.1: 384K
- Released
Mistral Medium 3.5 128B: April 29, 2026
SubQ Snap 1.1: October 22, 2026
- Shared results
- 0
- Mistral Medium 3.5 128B only
- 7
- SubQ Snap 1.1 only
- 0
- 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
SubQ Snap 1.1
SubQ Snap 1.1 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
SubQ Snap 1.1
SubQ Snap 1.1 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. SubQ Snap 1.1 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
SubQ Snap 1.1
SubQ Snap 1.1 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
SubQ Snap 1.1 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
SubQ Snap 1.1 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.
21.6Mistral Medium 3.5 128B—SubQ Snap 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
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Mistral Medium 3.5 128B 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, reasoning, multimodal, 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
- 21.3
- Supported · #115/132
- SubQ Snap 1.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- Mistral Medium 3.5 128B
- 21.6
- Estimated · #134/158
- SubQ Snap 1.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Medium 3.5 128B
- 23.8
- Estimated · #141/158
- SubQ Snap 1.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- SubQ Snap 1.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Medium 3.5 128B
- 28.6
- Supported · #160/179
- SubQ Snap 1.1
- 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
- SubQ Snap 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Medium 3.5 128B
- 84.0
- #55/157
- SubQ Snap 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- SubQ Snap 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | SubQ Snap 1.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 21.3Supported · #115/132 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 public rows | Not comparable |
| Coding | 21.6Estimated · #134/158 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Reasoning | 23.8Estimated · #141/158 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Knowledge | 28.6Supported · #160/179 | 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#55/157 | 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, reasoning, multimodal, 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
- SubQ Snap 1.1
- $0.00004
- Fits in one request
SubQ Snap 1.1 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
- SubQ Snap 1.1
- $0.002
- Fits in one request
SubQ Snap 1.1 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
- SubQ Snap 1.1
- $0.0088
- Fits in one request
- Cached input priced at the published list-input rate
SubQ Snap 1.1 has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. SubQ Snap 1.1 has no published cached-input rate, so cached tokens use its listed input rate.
Your workload
Enter a month of tokens to price it at both models’ listed API rates.
- Mistral Medium 3.5 128B
- $30.00
- per month
- SubQ Snap 1.1
- $0.400
- per month
Standard listed rates, no batch or volume discounts.
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 128BSubQ Snap 1.1
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
SubQ Snap 1.1
API model ID
Mistral Medium 3.5 128B
Not sourced
SubQ Snap 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 Medium 3.5 128B
Not published
SubQ Snap 1.1
Not published
Subquadratic pricingDocumented inputs
Mistral Medium 3.5 128B
text, image
Mistral-Medium-3.5-128B model cardSubQ Snap 1.1
Not sourced
Documented outputs
Mistral Medium 3.5 128B
SubQ Snap 1.1
Not sourced
Provider availability
Mistral Medium 3.5 128B
Not sourced
SubQ Snap 1.1
Not sourced
Reasoning profile
Mistral Medium 3.5 128B
Reasoning
SubQ Snap 1.1
Unspecified
Weight access
Mistral Medium 3.5 128B
Open Weight
SubQ Snap 1.1
Proprietary
License
Mistral Medium 3.5 128B
Open Weight
SubQ Snap 1.1
Proprietary
Release date
Mistral Medium 3.5 128B
2026-04-29
SubQ Snap 1.1
2026-10-22
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
- Repository review: $0.0975 vs $0.002. Cache-heavy agent loop: $0.405 vs $0.0088.
- Context tradeoff
- SubQ Snap 1.1 has the larger documented window (384K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Medium 3.5 128B or SubQ Snap 1.1?
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, Mistral Medium 3.5 128B or SubQ Snap 1.1?
SubQ Snap 1.1 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 SubQ Snap 1.1?
SubQ Snap 1.1 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 SubQ Snap 1.1?
For the stated presets, chat costs $0.00525 on Mistral Medium 3.5 128B and $0.00004 on SubQ Snap 1.1; repository review costs $0.0975 and $0.002; the cache-heavy agent loop costs $0.405 and $0.0088. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. SubQ Snap 1.1 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 SubQ Snap 1.1?
SubQ Snap 1.1 has the larger documented context window: 384K, 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-even52M/day
SubQ Snap 1.1
API / mo$30
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 evidence7 rows
Agentic
Coding
Knowledge
7 public results · 0 shared
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Last updated November 11, 2026