Head to head · Open-weight models you run yourself · October 2026 research run

DeepSeek-V4-Flash-0731 vs Step 3.7 Flash

DeepSeek-V4-Flash-0731 scores 61.9 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 1 of 4 scored categories. Step 3.7 Flash leads on agent interface. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

DeepSeek-V4-Flash-0731 C

Best for Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context

Ahead on

  • Risk, 50 against 45

Watch for

The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine

Step 3.7 Flash C

Best for Teams with a GPU server or a provider account that want an open, long-context agent model with vision

Ahead on

  • Agent interface, 85 against 75

Watch for

About 131 GB to run at 4 bits with images, over a 128 GB machine's share

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Step 3.7 Flash +10

DeepSeek-V4-Flash-0731 75

Chat template 10, there is no Jinja template and the format is given as Python to run. Tool-calling format 10, DeepSeek documents how tools are declared and returned and ships its own parser, but only vLLM's recipe names a runtime parser, so the 25 line isn't met. Trained tool calling 20, several invoke elements in one call block. Structured output 15, DeepSeek's deepseek-recipe library documents JSON Schema response formats for V4 prompts. Reasoning control 10, chat mode with no thinking and three effort levels in thinking mode, with reasoning returned as reasoning_content. Context 10, 1M tokens trained.

Step 3.7 Flash 85

Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Risk DeepSeek-V4-Flash-0731 +5

DeepSeek-V4-Flash-0731 50

File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands pass --trust-remote-code. Provenance 15, the deepseek-ai organisation and a SHA-256 on every shard. Safety evaluation 0, none published. Agent guidance 0, the encoding README's notes about the parser and the reserved developer role are usage notes, read alike for all three DeepSeek listings. Licence risk 15, MIT with no deductions. Security programme 5, for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories.

Step 3.7 Flash 45

File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.

Score by category

CategoryWeight this runDeepSeek-V4-Flash-0731Step 3.7 FlashEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257070even
Risk20%205045DeepSeek-V4-Flash-0731 +5
Agent interface10%107585Step 3.7 Flash +10
Documentation and upkeep10%103839Step 3.7 Flash +1
Negative events≤1500
TotalC 61.9/100C 59.8/100

Facts side by side

FactDeepSeek-V4-Flash-0731Step 3.7 Flash
KindOpen-weight modelOpen-weight model
VendorDeepSeekStepFun
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITApache-2.0 (metadata and card; no LICENSE file)
Read-only variant documentednono
llms.txtnono
Last release2026-07-312026-06-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.5/5 (4)none

How the scores have moved

Verdicts

DeepSeek-V4-Flash-0731

A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though five inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.

Step 3.7 Flash

A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.

Before you call either

DeepSeek-V4-Flash-0731

  1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{"use_fp4_indexer_cache": true}' --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)
  2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template
  3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string="true" and JSON values string="false"
  4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max
  5. Without the hardware, call it through a Hugging Face inference provider (DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash
  6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory

Step 3.7 Flash

  1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server
  2. On vLLM or SGLang pass --tool-call-parser step3p5 --reasoning-parser step3p5
  3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code
  4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
  5. StepFun's API has two hosts. Use the one where your key was issued, api.stepfun.ai or api.stepfun.com
  6. The same model is on our StepFun API listing, with prices and terms

Questions

Which is better for AI agents, DeepSeek-V4-Flash-0731 or Step 3.7 Flash?

DeepSeek-V4-Flash-0731 scores 61.9 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 1 of 4 scored categories. Step 3.7 Flash leads on agent interface.

Other comparisons with DeepSeek-V4-Flash-0731 or Step 3.7 Flash

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.