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DeepSeek-V3.1

wandb · Chat model

DeepSeek-V3.1 is listed here as a chat model from wandb. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Provider and model identifiers are kept in their original form for accuracy.

wandb-wandb-deepseek-ai-deepseek-v3-1

Input
$55000.0000 / 1M tokens
Output
$165000.0000 / 1M tokens
Cached input
N/A
Context window
128.0K

Catalog generated: Jul 13, 2026

Quick read

Best for

Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.

Things to verify

Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.

Pricing

Item Price
Input
$55000.0000 / 1M tokens
Output
$165000.0000 / 1M tokens
Embedding
$55000.0000 / 1M tokens

Limits

Context window
128.0K
Max input tokens
128.0K
Max output tokens
128.0K
Max tokens
128.0K

Capabilities

Capability Supported
Vision -
Function calling -
Parallel function calling -
Tool choice -
Prompt caching -
Reasoning -
Response schema -
System messages -
Audio input -
Audio output -
Web search -
PDF input -
Video input -

Benchmarks

Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer

Benchmark Score Metric Scope Checked Source
MMLU 88.5 EM Base model: DeepSeek-V3 (DeepSeek-V3) 2026-05-31 Link
GPQA Diamond 59.1 Pass@1 Base model: DeepSeek-V3 (DeepSeek-V3) 2026-05-31 Link
LiveCodeBench 37.6 Pass@1 Base model: DeepSeek-V3 (DeepSeek-V3) 2026-05-31 Link
AIME 2024 39.2 Pass@1 Base model: DeepSeek-V3 (DeepSeek-V3) 2026-05-31 Link
MATH-500 90.2 EM Base model: DeepSeek-V3 (DeepSeek-V3) 2026-05-31 Link
MMLU-Pro 84.8 EM Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
GPQA Diamond 80.1 pass@1 Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
Humanity's Last Exam 15.9 pass@1 Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
LiveCodeBench 74.8 pass@1 Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
Aider Polyglot 76.3 accuracy Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
SWE-bench Verified (Agent mode) 66.0 resolved Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link
AIME 2025 88.4 pass@1 Base model: DeepSeek-V3.1 (DeepSeek-V3.1-Thinking) 2026-05-31 Link

Sources

Source links
Pricing dataLiteLLM model cost map
Synced at2026-05-28
Catalog generated2026-07-13T14:21:55.110Z