Data as of October 25, 2026 · How the score is built
Released Apr 29, 2026256K context
Mistral Medium 3.5 128B
Decision readingMistral Medium 3.5 128B scores 33.6 out of 100 and ranks #171 of 228. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Agentic at #114. API pricing is $1.5 input and $7.5 output per million tokens.
Released Apr 29, 2026 — see all recent releases
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.
Capability
33.6/100
field median 50#171 of 228 ranked models
Public
#171of 228
Verified —
Price
$1.50input / $7.50 output
input median $0.75blended $4.50
Speed
Not measured
field median 96 tok/sTime to first token not measured
Context
256Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Agentic ranks #114. Particularly useful for coding agents, browser research, and computer-use workflows.
Validate before choosing
7 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.
Source-linked · 7 displayable benchmark rows
Category 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 #114 of 132Percentile 14thWeight 22%3 benchmarksVerified | 20.0 | 3 benchmarks | Verified | |||
| CodingRank #117 of 153Percentile 24thWeight 20%2 benchmarksVerified | 22.8 | 2 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #151 of 187Percentile 19thWeight 12%2 benchmarksVerified | 31.2 | 2 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
7 of 766 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow 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
- MultimodalNot measured
- Knowledge2/2 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
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
- Agentic14th percentile
- Coding24th percentile
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge19th percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#114/132
- Coding#117/153
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#151/187
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
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 | WeightDisplay only | |
| 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 |
|---|---|---|---|---|---|
| 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 | Weight4% ref. weight | |
| τ³-bench resultsτ³-Bench Tool-Agent-User Evaluation | Score91.4% | Versus best verified row Best verified: Mercury 2.5 · 96.0% | Gap4.6 behind | Weight2% ref. weight | |
| 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 |
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 | Weight6% ref. weight | 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 | Weight2% ref. weight |
Bars run 0–100; the dark tick marks the best source-verified value
All 7 rowsWhat 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.
Mistral Medium 3.5 128B · 33.6 score · $4.50 blended per million tokens
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Horizontal: blended price per million tokens, log scale · Vertical: public score
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
128b
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
- Mistral Medium 3.5 128B needs ~160GB VRAM (2× NVIDIA H100 (80GB)).
- Rate limits
- Not tracked yet
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 #171 of 228 on the public leaderboard with a score of 33.57/100. 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 766 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Agentic at #114, while its lowest eligible position is Knowledge at #151. particularly useful for coding agents, browser research, and computer-use workflows.
Last updated October 25, 2026. Runtime fields remain blank until a sourced snapshot exists.
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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Questions
How does Mistral Medium 3.5 128B perform overall in AI benchmarks?
Mistral Medium 3.5 128B ranks #171 out of 228 models on the public BenchAlign leaderboard, with a score of 33.57/100. 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 #151 out of 187 eligible models for knowledge and understanding, with a public category score of 31.2/100. 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 #117 out of 153 eligible models for coding and programming, with a public category score of 22.8/100. 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 #114 out of 132 eligible models for agentic tool use and computer tasks, with a public category score of 20/100. 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 23 source-displayable rows. 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.