Capability
54.8/100
field median 59
#109 of 231 ranked models
DeepSeek V3.2
Released Dec 1, 2025 — see all recent releases
Decision reading
DeepSeek V3.2 scores 54.8 out of 100 and ranks #109 of 231. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Coding at #84. API pricing is $0.28 input and $0.42 output per million tokens, with cached input at $0.028.
Data as of September 3, 2026 · How the score is built
Strongest published evidence
Coding ranks #84. Particularly well-suited for software development and code generation tasks.
Validate before choosing
7 published rows leave some tracked benchmark slots empty.
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
$0.28input / $0.42 output
input median $1
cached $0.028 · blended $0.35
Speed
35tok/s
field median 91 tok/s
First token 3.75 s
Context
128Ktokens
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.
DeepSeek V3.2 category percentile values
- AgenticNot eligible
- Coding44th percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- AgenticNot ranked
- Coding#84/149
- ReasoningNot ranked
- KnowledgeNot ranked
- 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 modelDeepSeek V3.2 · 54.8 score · $0.35 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
- KnowledgeNot measured
- Math2/2 verified
- 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
- 128K
- 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
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Published at $0.028 per million cached input tokens
- 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 |
|---|---|---|---|---|---|---|
| AgenticWeight 22%3 benchmarksVerified | Score pending | Not ranked | Not available | 22% | 3 benchmarks | Verified |
| CodingRank #84 of 149Percentile 44thWeight 20%2 benchmarksVerified | 50.3 | #84 of 149 | 44th | 20% | 2 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| MathRank Not rankedWeight 5%2 benchmarksVerified | 40.3 | Not ranked | Not available | 5% | 2 benchmarks | Verified |
| 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-Rebench | Score60.9% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap4.4 behind | WeightWeighted 20% | Benchmark exact |
| React Native Evals | Score71.5% | Versus best verified row Best verified: Composer 2 · 96.1% | Gap24.6 behind | WeightDisplay only | Benchmark exact |
Agentic3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Claw-Eval | Score40.2% | Versus best verified row Best verified: Ornith-1.5-397B · 81.4% | Gap41.2 behind | WeightDisplay only | Benchmark exact |
| VITA-Bench | Score18.5% | Versus best verified row Best verified: Qwen3.7 Max · 47.9% | Gap29.4 behind | WeightDisplay only | Benchmark exact |
| Gert LabsGert Labs Composite Game Benchmark | Score29.57% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap43.4 behind | WeightDisplay only | Benchmark exact |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score22.100% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap66.9 behind | WeightWeighted 30% | Benchmark exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score2.100% | Versus best verified row Best verified: GPT-6 Astra · 97.600% | Gap95.5 behind | WeightWeighted 10% | Benchmark exact |
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.
Dec 1, 2025 · you are here
DeepSeek V3.2Score 54.8 · $0.28 / $0.42
Base entry
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.
DeepSeek V3.2 ranks #109 of 231 on the public leaderboard with a score of 54.75/100. Its source-verified position is #53 of 105.
DeepSeek V3.2 is a open weight model with a 128K context window. No explicit reasoning mode is documented in this profile.
DeepSeek V3.2 sits in the DeepSeek V3.2 family with DeepSeek V3.2 (Thinking). 7 of 417 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #84. particularly well-suited for software development and code generation tasks.
Radar
Full release historyDeepSeek V3.2 release history
DeepSeek V3.2 released
DeepSeek · Model release
Source confirmed
Radar confirmed these at the source. Already tracking DeepSeek V3.2? See the free Radar Brief for what changes next.
Frequently asked questions
How does DeepSeek V3.2 perform overall in AI benchmarks?
DeepSeek V3.2 ranks #109 out of 231 models on the public BenchAlign leaderboard, with a score of 54.75/100. Its evidence status is Supported, 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 DeepSeek V3.2 good for coding and programming?
DeepSeek V3.2 ranks #84 out of 149 eligible models for coding and programming, with a public category score of 50.3/100. 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 DeepSeek V3.2 good for mathematics?
DeepSeek V3.2 has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is DeepSeek V3.2 good for agentic tool use and computer tasks?
DeepSeek V3.2 has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is DeepSeek V3.2 open source?
DeepSeek V3.2 is an open-weight model from DeepSeek. 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.
Which sibling models are related to DeepSeek V3.2?
DeepSeek V3.2 belongs to the DeepSeek V3.2 family. Related tracked variants include DeepSeek V3.2 (Thinking). A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does DeepSeek V3.2 have full benchmark coverage on BenchLM?
No. DeepSeek V3.2 currently has 19 source-displayable rows across 417 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 DeepSeek V3.2?
DeepSeek V3.2 has a reported context window of 128K 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 DeepSeek V3.2 with every tracked model410 comparisons
Last updated September 3, 2026. Runtime fields remain blank until a sourced snapshot exists.
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