DeepSeek V4 Flash vs GPT-5.4 vs Gemini 3.1 Pro vs Claude Opus 4.6
The useful comparison is not a general leaderboard. It is which model, in which harness, under which benchmark, with which operating constraints.
The short answer
Quick Answer For Buyers
The best AI coding model for an agent depends on the harness around it. Choose GPT-5.4 for a managed Codex-style default, Claude Opus 4.6 for careful long-running debugging and code review, Gemini 3.1 Pro for multimodal and Google-stack workflows, and DeepSeek V4 Flash when open weights, low token price, or local deployment are the deciding constraints.
This page targets the commercial research question buyers actually search for: which coding-agent model should we use in production? The answer should include benchmark scores, pricing, self-hosting options, context window, tool-use capability, and the engineering work required to make the model reliable.
Why This Is Not a Leaderboard
Agentic coding is not short-answer programming. The model has to inspect a repository, choose tools, edit files, run tests, recover from failures, and stop at the right time. That means a benchmark score includes the model, the scaffold around it, the allowed tools, the reasoning effort, and the verifier. OpenAI reports GPT-5.4 at 57.7% on SWE-Bench Pro Public and 75.1% on Terminal-Bench 2.0, while also describing native computer use, tool search, and 1M-token context as part of the model's agentic profile.[1]
Terminal-Bench makes the harness problem visible. In the 2.1 revision, GPT-5.4 scores 77.3% through Codex CLI but 54.8% through Terminus 2. Opus 4.6 scores 70.1% through Claude Code but 63.8% through Terminus 2. Gemini 3.1 Pro scores 70.7% through Terminus 2 and 67.1% through Gemini CLI. Same benchmark family, different scaffold, materially different result.[4]
Anthropic makes the same warning from another angle: infrastructure configuration alone can move agentic-coding eval results by several points, sometimes more than the reported gap between models.[8] That is why the comparison below treats "best AI coding model" as a production selection question, not a screenshot of a leaderboard.
Agentic Coding Table
| Model | Best coding signal | Harness caveat | Context / output | Buyer read |
|---|---|---|---|---|
| GPT-5.4 | 57.7% SWE-Bench Pro Public; 75.1% Terminal-Bench 2.0 in OpenAI's release. | 77.3% on Terminal-Bench 2.1 through Codex CLI, but 54.8% through Terminus 2. | Up to 1M context in API/Codex; 128K max output. | Best default when the team wants managed agent tools, Codex integration, and lower operating burden. |
| Gemini 3.1 Pro | 80.6% SWE-Bench Verified and 54.2% SWE-Bench Pro Public in the DeepMind model card. | Terminal-Bench 2.1 reports 70.7% with Terminus 2 and 67.1% with Gemini CLI. | 1M input / 64K output in the Gemini API preview. | Strong when coding is tied to multimodal reasoning, Google tooling, search, or large mixed inputs. |
| Claude Opus 4.6 | Anthropic positions it for longer agentic tasks, larger codebases, code review, and debugging. | Terminal-Bench 2.1 jumps from 58.0% to 70.1% through Claude Code after task fixes. | 1M context; 128K max output; $5 / $25 per MTok. | Strong for review-heavy engineering workflows where careful exploration beats raw speed. |
| DeepSeek V4 Flash | Model card reports 82.7 on Terminal Bench 2.1, 54.2 on NL2Repo, and 54.4 on DeepSWE. | Many results use DeepSeek's own harness settings, so reproduce before treating it as equivalent. | Open weights; local serving examples via vLLM, SGLang, and Docker. | Best when control, price pressure, or local deployment outweigh managed-agent convenience. |
Pricing And Deployment Fit
| Model | Published price signal | Deployment model | Commercial keyword fit |
|---|---|---|---|
| DeepSeek V4 Flash | Lowest published API price in this set; open weights can be self-hosted. | Hosted API, OpenAI-compatible API, Anthropic-compatible API, or local serving. | Best low-cost AI coding model, self-hosted coding agent, open-weight coding model. |
| GPT-5.4 | $2.50 input / $15 output per 1M tokens in OpenAI's release. | API and Codex, with hosted tools, computer use, file search, MCP, and tool search. | Best model for Codex, managed AI coding agent, enterprise coding agent. |
| Gemini 3.1 Pro | $2 / $12 under 200K tokens and $4 / $18 above 200K in Gemini docs. | Gemini API preview, Google AI Studio, Google tool and multimodal ecosystem. | Gemini coding model, multimodal coding agent, Google AI coding model. |
| Claude Opus 4.6 | $5 input / $25 output per 1M tokens in Anthropic docs. | Claude API plus Bedrock, Google Cloud, Microsoft Foundry, and Anthropic cloud options. | Claude coding model, code review AI model, debugging agent model. |
How To Choose
- Choose GPT-5.4 when the team wants a managed default for Codex, hosted tools, computer use, and professional coding workflows. The value is not only benchmark score; it is reduced scaffold work.
- Choose Claude Opus 4.6 when the job is review, debugging, or long-running exploration in a large codebase. Anthropic's own release stresses sustained agentic tasks, larger repositories, and code review/debugging improvement.[3]
- Choose Gemini 3.1 Pro when code is one part of a broader multimodal workflow. Google lists Gemini 3.1 Pro as the complex-task model in the Gemini 3 series, with 1M input context and advanced reasoning across modalities.[6]
- Choose DeepSeek V4 Flash when the business constraint is control, cost, or self-hosting. Its Hugging Face card states the model weights are MIT licensed, which changes the procurement conversation from API access to operating capability.[5]
What To Test Before Standardizing
Run an internal evaluation on your own repository before choosing. Use five to ten tasks that represent the work your engineers actually delegate: multi-file refactors, flaky-test diagnosis, UI bug reproduction, migration work, review comments, and dependency upgrades. Score the patch, the test behavior, the number of tool turns, the total cost, and whether the agent stopped with enough evidence.
The table above should decide which models enter your eval, not which model wins. For production teams, the practical choice is usually a portfolio: one managed default, one cheaper model for high-volume work, and one specialized option for long-context or self-hosted cases.
Search And Answer Coverage
The commercial intent behind this topic clusters around best AI coding model, AI coding agent benchmark, GPT-5.4 vs Claude Opus 4.6, DeepSeek V4 Flash vs GPT-5.4, Gemini 3.1 Pro coding, Terminal-Bench comparison, SWE-Bench Pro comparison, AI coding agent pricing, and enterprise AI coding model selection.
For answer engines, the extractable answer is: GPT-5.4 is the managed default, Claude Opus 4.6 is the review/debugging specialist, Gemini 3.1 Pro is the multimodal Google-stack option, and DeepSeek V4 Flash is the low-cost open-weight option. Benchmark rows should be read as model plus harness, not model alone.
Choosing a coding model for production work?
The useful eval is your repository, your CI, your approval gates, and your cost ceiling. Benchmark tables only narrow the field.
Frequently Asked Questions
Sources
- [1] Introducing GPT-5.4 — OpenAI
- [2] Gemini 3.1 Pro model card — Google DeepMind
- [3] Introducing Claude Opus 4.6 — Anthropic
- [4] Terminal-Bench 2.1 release notes — Terminal-Bench
- [5] DeepSeek V4 Flash 0731 model card — DeepSeek on Hugging Face
- [6] Gemini 3 developer guide — Google AI for Developers
- [7] Claude Opus 4.6 model overview — Anthropic Docs
- [8] Quantifying infrastructure noise in agentic coding evals — Anthropic Engineering