Table of Contents

A Thorough Coding-Focused Comparison of the Latest GPT (GPT-5.2): How Is It Different from Claude 4.6 and Gemini 3.1 Pro?

Coding with generative AI is not just about generating a function and calling it done. In real work, you’re judged on your ability to understand an existing repository, keep multiple files consistent, run tests → fix based on failing logs, and then summarize changes in a way that can be explained in code review (“why this change?”). Models that are strong here don’t just speed up implementation—they tend to reduce rework and review cost.

In this article, we’ll center on OpenAI’s GPT-5.2 family as the latest GPT, and compare it with common alternatives: Claude 4.6 (Opus/Sonnet) and the recently prominent Gemini 3.1 Pro—specifically from a coding perspective. Rather than rushing to a verdict, we’ll organize the comparison around decision points that teams often get stuck on (long-context handling, agent execution, pricing, and how execution logs are treated), so you can use it for real selection. :contentReference[oaicite:0]{index=0}

Who this article helps (concretely)

First, it’s for people who want to optimize “model choice” in AI coding environments like Cursor or VS Code. Once you go beyond autocompletion and start delegating bug fixes, refactors, and test fixes, you’ll feel the difference in a model’s ability to finish the fix.

Second, it’s for teams shipping products with multiple languages mixed (TypeScript/Java/Python, etc.) that want to reduce PR review friction and CI failures. Even if the output looks clean, if tests fail or the design intent drifts, humans end up undoing and fixing it anyway.

Third, it’s for people who want to use generative AI daily while realistically balancing cost and throughput (token unit price and speed). Performance matters, but so do operational quirks like pricing, cacheable inputs, and long-context billing—comparing with those included helps avoid mistakes. :contentReference[oaicite:1]{index=1}

What “the latest GPT” means: Positioning of the GPT-5.2 family (coding + agents)

OpenAI positions GPT-5.2 as a “flagship strong at coding and agentic tasks,” emphasizing improvements in long-context understanding, tool use, and multi-step execution. On the ChatGPT side, there are offerings such as Instant/Thinking/Pro, and on the API side, gpt-5.2 sits at the center with both chat-oriented and pro-oriented lines alongside it. :contentReference[oaicite:2]{index=2}

From a coding standpoint, what matters is the ability to adjust the amount of reasoning (“thinking”). On OpenAI’s model pages, reasoning.effort includes none/low/medium/high/xhigh, letting you switch between speed and caution depending on task complexity. It also lists a 400,000 token context and a maximum output of 128,000, which is aimed at handling larger design docs and multiple files together. :contentReference[oaicite:3]{index=3}

On pricing, OpenAI’s official price table shows gpt-5.2 with separate input/output pricing and also explicitly includes cached input (a reuse-like concept). For coding teams that repeatedly run “same rules + different diffs,” this becomes meaningful. :contentReference[oaicite:4]{index=4}

The comparison targets: Claude 4.6 and Gemini 3.1 Pro (“latest” strengths)

Claude 4.6 announced Opus 4.6 (2/5) and Sonnet 4.6 (2/17) in February 2026, explicitly stating upgrades for coding, agent planning, computer use, and long-context reasoning. In particular, a 1M-token context (beta) is a major feature. :contentReference[oaicite:5]{index=5}

Gemini 3.1 Pro was refreshed as “for more complex tasks” in its official blog, and its model card presents coding evaluations (like SWE-Bench Verified and Terminal-Bench) in table form. Gemini API pricing also clearly states that pricing changes beyond 200k tokens, and it explicitly lists context caching and search grounding charges. :contentReference[oaicite:6]{index=6}

Feature-by-feature comparison: Breaking down the “places where coding differences show up”

From here, we’ll split the areas where differences tend to show up in coding into seven categories and compare GPT-5.2 with Claude 4.6 and Gemini 3.1 Pro.

1) Repository fix capability: Can it fix, make tests pass, and drive to completion?

GPT-5.2

GPT-5.2 emphasizes being able to execute complex real-world tasks end-to-end, not just one-shot generation, leaning toward “completion” including tool use and long-context understanding. In real coding work, the key is reading test/build logs, shifting the fix strategy, and converging with minimal diffs—so using higher reasoning.effort for hard bugfix work fits well. :contentReference[oaicite:7]{index=7}

Claude 4.6

Claude Opus 4.6 states in announcements that it “works more reliably on large codebases,” is strong in code review and debugging, and can sustain long agent work. Opus 4.6 also includes the 1M-context beta, reflecting a philosophy of fixing while holding long specifications and related docs in context. :contentReference[oaicite:8]{index=8}

Gemini 3.1 Pro

Gemini 3.1 Pro highlights items like SWE-Bench Verified (agentic coding) in its model card, explicitly showing metrics for agent-style code-fixing tasks. Practically, it’s structured to help you evaluate whether it’s strong at the “test → logs → fix” loop. :contentReference[oaicite:9]{index=9}

A practical interpretation tends to look like:

If you want “review-ready writing and PR packaging,” Claude often feels great,
If you want to control cost by adjusting reasoning depth per task difficulty, GPT-5.2 is easy to operate,
If you want to run an execution/verification-first loop with terminal/tools and metrics aligned to it, Gemini 3.1 Pro often fits well. :contentReference[oaicite:10]{index=10}

2) Agent suitability: Can it loop plan → execute → verify → re-execute?

GPT-5.2: Reasoning control helps “intervention design”

In agent operations, the biggest problem is either (a) overthinking simple work and becoming slow, or (b) underthinking hard work and failing. GPT-5.2’s stepwise reasoning control makes it easier to operate per task—for example, “investigation = high,” “simple replacement = low,” and “hardest bug analysis = xhigh.” :contentReference[oaicite:11]{index=11}

Claude 4.6: Puts long tasks and “careful planning” front and center

Opus 4.6 explicitly says it can plan more carefully and sustain long agent tasks. Combined with long context, it tends to excel at flows like “create a plan → implement in stages → self-review → submit,” which suits complex refactors. :contentReference[oaicite:12]{index=12}

Gemini 3.1 Pro: Tool/terminal evaluations align with the “design philosophy”

Gemini’s model card includes items like Terminal-Bench, suggesting an emphasis on execution tasks involving OS/terminal interactions. Since coding is ultimately “run it and confirm it,” this philosophy fits real-world practice. :contentReference[oaicite:13]{index=13}

3) Long context: GPT-5.2’s 400k vs Claude’s 1M vs Gemini’s long-context billing

GPT-5.2: 400k context + large output cap

OpenAI’s model page lists GPT-5.2 as having a 400,000-token context window. This enables workflows where you hold design docs, logs, and related files together—especially “pin the guidelines/history up front and iterate on diffs,” which pairs well with caching. :contentReference[oaicite:14]{index=14}

Claude 4.6: Explicitly sells 1M context (beta)

Claude Opus 4.6 and Sonnet 4.6 both explicitly mention a 1M-token context (beta). For teams that want to hold massive specs, multiple design docs, meeting notes, and even past incident reports while making changes and producing explanations, this is attractive. :contentReference[oaicite:15]{index=15}

Gemini 3.1 Pro: Pricing changes past 200k; caching and search integration are explicit

Gemini API pricing clearly states that costs change for long inputs beyond 200k tokens, and it explicitly lists context caching and search grounding charges. If you use long context regularly, you stabilize cost by designing what is included each time vs cached. :contentReference[oaicite:16]{index=16}

Operational tip (common across all models): rather than “dump everything,” the following order tends to be safer:

Immutable rules (naming, exceptions, logging, prohibited items)
Only the files being changed
Test failure logs and reproduction steps
Additional design docs only when needed

This keeps the advantages of long context while reducing cost and noise. :contentReference[oaicite:17]{index=17}

4) Coding experience: It’s “editing and convergence,” not generation, that wins

GPT-5.2: Unifies code generation and agent execution under one model philosophy

GPT-5.2 plants its flag in “coding and agentic tasks,” leaning toward generating code, executing, fixing, and converging. OpenAI’s pricing also lists coding lines like gpt-5.2-codex and gpt-5.3-codex, making it easier to choose a line by use case. :contentReference[oaicite:18]{index=18}

Claude 4.6: Emphasizes review/debug “self-detection”

Opus 4.6 explicitly states strength in code review and debugging and that it’s good at catching its own mistakes. This tends to align with experiences where you want not just generated code, but also the “impact explanation” and “edge-case coverage” that often comes up in PR reviews. :contentReference[oaicite:19]{index=19}

Gemini 3.1 Pro: Transparent benchmarks match a real-world workflow

Gemini 3.1 Pro explicitly lists agentic coding and terminal-related evaluations in the model card. This itself conveys the message that “execution + verification” should be run as a loop, which fits CI-driven environments. :contentReference[oaicite:20]{index=20}

5) Multimodality: Development flows mixing images, UI, and diagrams

GPT-5.2 is described as accepting not only text but also images, and its model page states it can handle visual information. In workflows that include UI screenshots, error dialogs, or architecture diagrams, “situation understanding” matters more than simply outputting code. :contentReference[oaicite:21]{index=21}

Claude 4.6 emphasizes computer use and practical tasks spanning documents/spreadsheets/presentations, and it mentions autonomous multitask environments like Cowork. If you want to run adjacent development work (specs, research, documentation) in one flow, this direction can fit well. :contentReference[oaicite:22]{index=22}

Gemini explicitly lists search grounding charges, making it easier to “institutionalize” search integration in environments where research and coding mix (checking API specs, grounding error messages). :contentReference[oaicite:23]{index=23}

6) Pricing and throughput: The “boring but strong” differences that matter in daily use

GPT-5.2: Pricing includes cached input

OpenAI’s official pricing lists cached input pricing for GPT-5.2 in addition to input/output. Teams that repeat the same assumptions—terms, coding standards, architectural policy—can stabilize spend by designing prompts around caching. :contentReference[oaicite:24]{index=24}

Claude 4.6: Opus pricing is presented clearly

Anthropic’s Opus 4.6 announcement says pricing is kept the same in input/output form, and Claude’s pricing page includes estimation examples like ticket processing. This supports a layered strategy: push hardest tasks to Opus, and day-to-day work to Sonnet. :contentReference[oaicite:25]{index=25}

Gemini 3.1 Pro: Pricing changes for long context (>200k)

Gemini API pricing explicitly states that input/output unit prices change past 200k tokens. If you pass huge codebases or logs in full every time, costs can jump, so caching and split design become especially important. :contentReference[oaicite:26]{index=26}

7) Practical “division of labor”: Don’t hard-pick a single model

Finally, here’s a task-based way to choose in the field. It’s less about which model is “best” and more about an operational pattern that fails less often.

Pattern A: Everyday changes and PR throughput (speed + cost)

Top pick: GPT-5.2 (effort = low to medium)
Why: You can produce diffs at a good tempo with restrained reasoning, and switch to high only when needed
Tip: Don’t paste the same standards every time—create “fixed assumptions” designed for caching. :contentReference[oaicite:27]{index=27}

Pattern B: Large refactors, long specs, and accountability (context + review)

Top pick: Claude Opus 4.6 / Sonnet 4.6
Why: It openly positions 1M context (beta) and strengthened planning/review/debugging as key features
Tip: First have it produce a “plan + acceptance criteria,” then proceed in staged tasks. :contentReference[oaicite:28]{index=28}

Pattern C: Execution and verification first (CI, terminal, tool-driven)

Top pick: Gemini 3.1 Pro
Why: It explicitly lists agentic coding and terminal benchmarks in the model card, and pricing makes long context/caching/search integration easy to design operationally
Tip: Split long context + cache; include search grounding when appropriate. :contentReference[oaicite:29]{index=29}

Ready-to-use request templates (often more effective than model differences)

A request format often matters more than the model. Here’s a minimal set that helps PRs pass more easily.

Template

Goal: what must be achieved (e.g., prevent double submission, eliminate N+1)
Scope: list the files you’re allowed to change (this is the most important)
Acceptance criteria: tests, types, lint, compatibility, performance (include at least one numeric/condition)
Extra info: reproduction steps, logs, failing test names, example expected outputs

Short example

Goal: prevent double submission in payments
Scope: only CheckoutForm.tsx and useCheckout.ts
Acceptance: 0 type errors, button disabled while submitting, navigate only on success, update existing tests
Extra: repro steps and error logs

If you use this structure and then switch models, comparisons become fairer and selection becomes easier to justify.

Summary: The latest GPT (GPT-5.2) is strong for “tunable reasoning × agents × operational cost”

As the latest GPT, GPT-5.2 puts coding and agentic tasks at the center, and highlights operationally “easy-to-run” design elements such as stepwise reasoning.effort, 400k context, and a pricing model that includes cached input. :contentReference[oaicite:30]{index=30}

Meanwhile, Claude 4.6 emphasizes 1M context (beta) and strengthened planning/review/debugging, making it attractive for teams that want to produce “explainable changes” under long specs and large refactors. :contentReference[oaicite:31]{index=31}

Gemini 3.1 Pro explicitly presents agentic coding and terminal benchmarks in its model card, and its pricing structure clearly signals how to design long-context/caching/search integration—making it a good fit for workflows centered on “run it and verify it.” :contentReference[oaicite:32]{index=32}

In the end, rather than fixing on one model, the most realistic recommendation is to switch by task—for example: everyday changes with GPT-5.2 (low–medium reasoning), hard moments with Claude 4.6 (long context + planning), and verification-driven work with Gemini 3.1 Pro (execution + tools). This tends to be the most resilient in practice and keeps team learning costs manageable.