Capability
30.1/100
field median 56.2
#213 of 232 ranked models
Mistral Medium 3.5 128B
Released Apr 29, 2026 — see all recent releases
Decision reading
Mistral Medium 3.5 128B scores 30.1 out of 100 and ranks #213 of 232. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Coding at #127. API pricing is $1.5 input and $7.5 output per million tokens.
Data as of September 10, 2026 · How the score is built
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Strongest published evidence
Coding ranks #127. Particularly well-suited for software development and code generation tasks.
Validate before choosing
7 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
$1.50input / $7.50 output
input median $1
blended $4.50
Speed
Not measured
field median 86.5 tok/s
Time to first token not measured
Context
256Ktokens
field median 256,000
Reported for this model; direct source link not stored
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Mistral Medium 3.5 128B category percentile values
- Agentic1st percentile
- Coding16th percentile
- ReasoningNot eligible
- Knowledge24th percentile
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#150/152
- Coding#127/151
- ReasoningNot ranked
- Knowledge#140/183
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Current modelMistral Medium 3.5 128B · 30.1 score · $4.50 blended per million tokens
Horizontal: blended price per million tokens, log scale · Vertical: public score
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic3/3 verified
- Coding2/2 verified
- ReasoningNot measured
- Knowledge2/2 verified
- MathNot measured
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 256K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #150 of 152Percentile 1stWeight 22%3 benchmarksVerified | 21.9 | #150 of 152 | 1st | 22% | 3 benchmarks | Verified |
| CodingRank #127 of 151Percentile 16thWeight 20%2 benchmarksVerified | 36.9 | #127 of 151 | 16th | 20% | 2 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank #140 of 183Percentile 24thWeight 12%2 benchmarksVerified | 39.0 | #140 of 183 | 24th | 12% | 2 benchmarks | Verified |
| MathWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score77.6% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap18.4 behind | WeightWeighted 10% | |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score66.4% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap30.6 behind | WeightDisplay only | Verified |
Agentic3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| τ³-bench resultsτ³-Bench Tool-Agent-User Evaluation | Score91.4% | Versus best verified row Best verified: Mercury 2.5 · 96.0% | Gap4.6 behind | WeightDisplay only | |
| Gert LabsGert Labs Composite Game Benchmark | Score39.10% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap33.9 behind | WeightDisplay only | Benchmark exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score39.0% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap48.3 behind | WeightDisplay only |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score75.3% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap17.1 behind | WeightWeighted 10% | Verified |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score34.8% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap60.7 behind | WeightDisplay only |
Lineage
The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Apr 29, 2026 · you are here
Mistral Medium 3.5 128BScore 30.1 · $1.5 / $7.5
128b
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Mistral Medium 3.5 128B ranks #213 of 232 on the public leaderboard with a score of 30.13/100. It does not yet have enough sourced coverage for a verified position.
Mistral Medium 3.5 128B is a open weight model with a 256K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
7 of 434 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #127, while its lowest eligible position is Agentic at #150. particularly well-suited for software development and code generation tasks.
Radar
Full release historyMistral Medium 3.5 128B release history
Mistral Medium 3.5 128B released
Mistral · Model release
Source confirmed
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Frequently asked questions
How does Mistral Medium 3.5 128B perform overall in AI benchmarks?
Mistral Medium 3.5 128B ranks #213 out of 232 models on the public BenchAlign leaderboard, with a score of 30.13/100. Its evidence status is Estimated, and this profile shows 7 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Mistral Medium 3.5 128B good for knowledge and understanding?
Mistral Medium 3.5 128B ranks #140 out of 183 eligible models for knowledge and understanding, with a public category score of 39/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Mistral Medium 3.5 128B good for coding and programming?
Mistral Medium 3.5 128B ranks #127 out of 151 eligible models for coding and programming, with a public category score of 36.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Mistral Medium 3.5 128B good for agentic tool use and computer tasks?
Mistral Medium 3.5 128B ranks #150 out of 152 eligible models for agentic tool use and computer tasks, with a public category score of 21.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Mistral Medium 3.5 128B open source?
Mistral Medium 3.5 128B is an open-weight model from Mistral. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Mistral Medium 3.5 128B have full benchmark coverage on BenchLM?
No. Mistral Medium 3.5 128B currently has 27 source-displayable rows across 434 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has a reported context window of 256K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Compare Mistral Medium 3.5 128B with every tracked model482 comparisons
Last updated September 10, 2026. Runtime fields remain blank until a sourced snapshot exists.
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