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Prompts that approach the documented context limit
Nemotron 3.5 Super VL 120B A12B
Nemotron 3.5 Super VL 120B A12B has the larger documented context window.
Confidence: documented
Mistral Medium 3.5 128B vs Nemotron 3.5 Super VL 120B A12B
Updated October 15, 2026. Rank says Nemotron 3.5 Super VL 120B A12B 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
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Decision reading
Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 33.72. 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.
Model A
Mistral
33.72/100
Estimated · Public rank #163
Conditional range 19.4–48.1
Model B
NVIDIA
49.51/100
Estimated · Public rank #97
Conditional range 35.2–63.9
- Shared results
- 2
- Mistral Medium 3.5 128B only
- 5
- Nemotron 3.5 Super VL 120B A12B only
- 17
- Like-for-like categories
- 0 / 8
Estimated: Mistral Medium 3.5 128B and Nemotron 3.5 Super VL 120B A12B. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Nemotron 3.5 Super VL 120B A12B 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
Nemotron 3.5 Super VL 120B A12B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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: rate-fallbackRepository 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.
22.6Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B
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.
5 Mistral Medium 3.5 128B only
2 Shared
17 Nemotron 3.5 Super VL 120B A12B only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
SWE-bench VerifiedCoding
Normalized gap 6.5Mistral Medium 3.5 128B:77.6%Nemotron 3.5 Super VL 120B A12B:71.1%
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 v5.8 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
Directional only- Mistral Medium 3.5 128B
- 20.0
- Supported · #107/127
- Nemotron 3.5 Super VL 120B A12B
- 44.2
- Estimated · #61/127
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Mistral Medium 3.5 128B
- 31.9
- Supported · #148/184
- Nemotron 3.5 Super VL 120B A12B
- 51.8
- Estimated · #73/184
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- Mistral Medium 3.5 128B
- 84.0
- #50/146
- Nemotron 3.5 Super VL 120B A12B
- 66.6
- #72/146
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Coding
Not comparable- Mistral Medium 3.5 128B
- 22.6
- Estimated · #114/150
- Nemotron 3.5 Super VL 120B A12B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Medium 3.5 128B
- 73.5
- Unranked · 2 rankable rows
- Nemotron 3.5 Super VL 120B A12B
- 73.0
- Unranked · 2 rankable rows
- 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
- Nemotron 3.5 Super VL 120B A12B
- 62.0
- Unranked · 5 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- Nemotron 3.5 Super VL 120B A12B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- Nemotron 3.5 Super VL 120B A12B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | Nemotron 3.5 Super VL 120B A12B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 20.0Supported · #107/127 | 44.2Estimated · #61/127 | Directional onlyBenchAlign v5.8 lane · 3 vs 4 public rows | Directional only |
| Knowledge | 31.9Supported · #148/184 | 51.8Estimated · #73/184 | Directional onlyBenchAlign v5.8 lane · 2 vs 3 public rows | Directional only |
| Instruction following | 84.0#50/146 | 66.6#72/146 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Coding | 22.6Estimated · #114/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Reasoning | 73.5Unranked · 2 rankable rows | 73.0Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 60.1Unranked · 1 rankable row | 62.0Unranked · 5 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 |
| 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 Medium 3.5 128B
- $0.00525
- Fits in one request
- Nemotron 3.5 Super VL 120B A12B
- API rate not published
- Fits in one request
Nemotron 3.5 Super VL 120B A12B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
- Nemotron 3.5 Super VL 120B A12B
- API rate not published
- Fits in one request
Nemotron 3.5 Super VL 120B A12B has no comparable published API token rate.
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
- Nemotron 3.5 Super VL 120B A12B
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. Nemotron 3.5 Super VL 120B A12B 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 Medium 3.5 128BNemotron 3.5 Super VL 120B A12B
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
Nemotron 3.5 Super VL 120B A12B
API model ID
Mistral Medium 3.5 128B
Not sourced
Nemotron 3.5 Super VL 120B A12B
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
Nemotron 3.5 Super VL 120B A12B
No comparable hosted API rate
NVIDIA Hugging Face model cardDocumented inputs
Mistral Medium 3.5 128B
Not sourced
Nemotron 3.5 Super VL 120B A12B
text, image, video
NVIDIA BF16 model cardDocumented outputs
Mistral Medium 3.5 128B
Not sourced
Nemotron 3.5 Super VL 120B A12B
Provider availability
Mistral Medium 3.5 128B
Not sourced
Nemotron 3.5 Super VL 120B A12B
Open Weights · Hugging Face, self-hosted inference
NVIDIA BF16 model cardReasoning profile
Mistral Medium 3.5 128B
Reasoning
Nemotron 3.5 Super VL 120B A12B
Reasoning
Weight access
Mistral Medium 3.5 128B
Open Weight
Nemotron 3.5 Super VL 120B A12B
Open Weight
License
Mistral Medium 3.5 128B
Open Weight
Nemotron 3.5 Super VL 120B A12B
Open Weight
Release date
Mistral Medium 3.5 128B
2026-04-29
Nemotron 3.5 Super VL 120B A12B
2026-10-15
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
- Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 33.72. 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
- Nemotron 3.5 Super VL 120B A12B 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 Nemotron 3.5 Super VL 120B A12B?
Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 33.72. 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 Medium 3.5 128B or Nemotron 3.5 Super VL 120B A12B?
Nemotron 3.5 Super VL 120B A12B 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 Nemotron 3.5 Super VL 120B A12B?
Nemotron 3.5 Super VL 120B A12B scores higher for agentic tasks on the public lane, 44.2 to 20. Nemotron 3.5 Super VL 120B A12B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Mistral Medium 3.5 128B or Nemotron 3.5 Super VL 120B A12B?
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 Medium 3.5 128B or Nemotron 3.5 Super VL 120B A12B?
Nemotron 3.5 Super VL 120B A12B has the larger documented context window: 1M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence24 rows
Agentic
τ³-bench results
Mistral Medium 3.5 128B91.4%SourceNemotron 3.5 Super VL 120B A12B18.9%SourceMistral Medium 3.5 128B leads this result
Gert Labs
Mistral Medium 3.5 128B39.10%SourceNemotron 3.5 Super VL 120B A12B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Mistral Medium 3.5 128B39.0%SourceNemotron 3.5 Super VL 120B A12B—Not directly comparable
Terminal-Bench 2.1
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B66.7%SourceNot directly comparable
τ²-bench Airline
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B76.5%SourceNot directly comparable
BrowseComp
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B78%SourceNot directly comparable
Coding
SWE-bench Verified
Mistral Medium 3.5 128B77.6%SourceNemotron 3.5 Super VL 120B A12B71.1%SourceMistral Medium 3.5 128B leads this result
SWE-bench (Vals)
Mistral Medium 3.5 128B66.4%SourceNemotron 3.5 Super VL 120B A12B—Not directly comparable
LiveCodeBench v6
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B88.2%SourceNot directly comparable
SWE Multilingual
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B67.7%SourceNot directly comparable
SWE-bench Pro
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B51.8%SourceNot directly comparable
Reasoning
CritPt
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B10.9%SourceNot directly comparable
Multimodal
BabyVision
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B29.6%SourceNot directly comparable
MMLongBench-Doc
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B62.8%SourceNot directly comparable
V*
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B85.6%SourceNot directly comparable
MathVision
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B78.2%SourceNot directly comparable
CharXiv
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B80.8%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Mistral Medium 3.5 128B34.8%SourceNemotron 3.5 Super VL 120B A12B—Not directly comparable
MMLU-Pro (Vals)
Mistral Medium 3.5 128B75.3%SourceNemotron 3.5 Super VL 120B A12B—Not directly comparable
GPQA-D
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B89.5%SourceNot directly comparable
HLE w/o tools
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B31.8%SourceNot directly comparable
HLE
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B29.2%SourceNot directly comparable
Instruction following
IFBench
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B70.9%SourceNot directly comparable
Math
Apex Shortlist
Mistral Medium 3.5 128B—Nemotron 3.5 Super VL 120B A12B88.4%SourceNot directly comparable
24 public results · 2 shared
Watch Mistral Medium 3.5 128B vs Nemotron 3.5 Super VL 120B A12B
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Last updated October 15, 2026