Claude Opus 4.7 vs GPT-5.5 vs Gemini 3.1 Pro vs DeepSeek V4: The April 2026 Frontier Model Showdown

I pulled verified numbers from Anthropic's Opus 4.7 announcement, the DeepSeek V4 Hugging Face blog, the Gemini 3.1 Pro model card, and Vellum's GPT-5.5 breakdown (OpenAI's announcement page kept returning 403). For the practitioner reality check, I leaned on Simon Willison's hands-on writeup, the Latent Space coverage, and three live Reddit threads — r/LocalLLaMA on V4-Pro, r/ClaudeAI on Opus 4.7, and r/Bard on Gemini regressions. Every benchmark below is lab-claimed unless flagged otherwise.

Wall Street spent Q1 predicting the AI price war would force Western labs down. The opposite happened. OpenAI doubled GPT-5.5's list price ($5/$30 vs GPT-5.4's $2.50/$15). Anthropic held Opus 4.7 flat at $5/$25 — but shipped a new tokenizer that Simon Willison clocked at ~1.46x more tokens per input than Opus 4.6, a stealth price hike on the same sticker. Gemini 3.1 Pro added a >200K-context surcharge ($4/$18 above the threshold). Only DeepSeek undercut, and that's a 75% promo expiring May 5.

The cleanest framing came from HN user redsaber on the GPT-5.5 thread: "the era of subsidized AI is over." GPT-5.5 was simultaneously removed from GitHub Copilot Pro (Pro+/Business/Enterprise only), reinforcing the read that flagship models are no longer entry-tier offerings. If you've been waiting for a model to be cheaper than last year's, you're going to be waiting longer.

The honest reading: no model wins everything, and the spread between #1 and #4 on any single benchmark is rarely more than 10–15 points. The decisive factor isn't "who's smartest" — it's which axis (cost, context, agentic harness, prose) matters most for your workload. Vellum's full comparison table is the cleanest single citation if you want to dig further.

On paper, this is Opus 4.7's race. 64.3% on SWE-Bench Pro is +5.7 over GPT-5.5's 58.6%, +10.1 over Gemini 3.1 Pro's 54.2%, and +8.9 over DeepSeek V4-Pro's 55.4%. SWE-Bench Verified at 87.6% is +7 vs Opus 4.6. Latent Space called it "literally one step better than 4.6 in every dimension."

Then I read u/Campfire_Steve's r/ClaudeAI post titled "Reverted from Opus 4.7 to 4.6 — went from endless loops to shipping 10 features in one session." Their setup: a Docker-deployed eBay scraper, ~5,800 LOC across 18 files, sustained collaborative coding sessions. On 4.7, they spent "three painful sessions of chasing our tails" before concluding the architecture had to be scrapped. On 4.6, in a single session: a new query planner, a one-time GCD API script with smart sampling (160 calls instead of 1,072), and 27 collection grid pages.

This is the workflow Anthropic explicitly markets 4.7 for. u/CricktyDickty captured the trade-off in one line: "4.6 is better at inferring what you mean. 4.7 is much more literal and doesn't do well with ambiguous requests." MindStudio's analyst review reaches the same conclusion: 4.7 "follows instructions much more literally than 4.6, which means prompts that worked before can now produce unexpected results."

Verdict: Opus 4.7 wins for shipping production code on well-specified tasks in large codebases. GPT-5.5 is the alternative for anyone whose flow is closer to the Codex / terminal workflow. If you're cost-sensitive and willing to accept verbosity, DeepSeek V4-Pro is genuinely usable as a worker subagent.

GPT-5.5's 82.7% on Terminal-Bench 2.0 is the largest single-benchmark margin in this cycle: +13.3 over Opus 4.7 (69.4%), +14.2 over Gemini 3.1 Pro (68.5%), +14.8 over DeepSeek V4-Pro (67.9%). Long-context accuracy at 512K–1M jumps from 36.6% (GPT-5.4) to 74.0% — that's the kind of step change that genuinely shifts what's buildable.

HN user Sembiance's hands-on test on a reverse-engineering coding task: "Achieved 90% perfect on first try in about a fourth of the time" versus Opus 4.6. u/refulgentis cited two specific wins: a streaming JSON decoder where sync time dropped from 75 seconds to 0.8 seconds, and a Flutter Web WASM debug session — both completed inside an hour.

The catch is what HN user wincy flagged: GPT-5.5 has a "lazy" failure mode. Asked to write a SQL transaction with rollback, it returned a template instead of completing the query, and the user had to "prod the model to do what they asked." Behavioral regressions like this are real and not picked up by Terminal-Bench scores.

Hidden wildcard: Gemini 3.1 Pro looks competitive on agentic benchmarks (MCP Atlas 69.2%) but practitioner reports tell a different story. Dan Cleary's CodeX writeup: "Google's models need the most additional layers and helpers compared to Anthropic and OpenAI, and they consistently try to break out of the harness so often that Converge hasn't rolled out any Gemini models to its users." If you're building a tool-calling agent, that's disqualifying.

For deep research and "hidden premise" exploration, Opus 4.7 is the pick. r/ClaudeAI user chipmux switched from GPT after a side-by-side: shared a stock portfolio with both models. "GPT simply adjusted the distribution. I gave the exact same prompt to Claude Opus 4.7. Claude not only optimized the portfolio but also suggested additional stocks and ETFs that were genuinely useful. GPT did not think in that direction."

Gemini 3.1 Pro's 94.3% on GPQA Diamond and 44.4% on Humanity's Last Exam are the strongest pure-reasoning numbers on paper, and the model also leads BrowseComp at 85.9%. But r/Bard is full of post-launch regression complaints since the Pro daily limit jumped to 50: "It now regularly gets confused… It's also a lot more fawning and sycophantic than before" (u/Oneirathon1). u/Powerful_Ad_8915's theory: "They are training a new model, that is why. Compute starved."

For prose specifically, do not use Opus 4.7. The HN front-page thread literally titled "Opus 4.7 is horrible at writing" (47801971) has multiple senior commenters reaching the same verdict. u/SyntaxErrorist: "It feels like they tuned it so hard for logic and coding that it lost its soul for actual writing." u/chmod775: "Opus 4.7 seems to reach ChatGPT levels of verbosity in code and loves to overcomplicate."

The vendor pricing pages don't tell you what you'll actually spend. Three things to know:

1. GPT-5.5 doubled the list price but uses ~40% fewer output tokens than GPT-5.4 on equivalent Codex tasks (per Vellum). The effective cost increase is ~20%, not 100%. Willison framed the repositioning bluntly: "the pricing relationship mirrors Claude Sonnet is to Claude Opus." GPT-5.4 is the new mid-tier; GPT-5.5 is the premium tier.

2. Opus 4.7's tokenizer changed. Same input → ~1.46x as many tokens as Opus 4.6. Anthropic claims this is offset by a ~50% reduction in reasoning verbosity, so the net per-task cost may still drop. Decrypt headline: "Token Eating Machine." The reconciliation is plausible but you should benchmark on your actual prompts before assuming the per-task cost matches Opus 4.6.

3. The 272K crossover. HN tester Topfi flagged that GPT-5.5 becomes more expensive than Opus 4.7 beyond ~272K input context. Below 272K, GPT-5.5's $5/$30 plus token efficiency wins. Above it, Anthropic's flat $5/$25 wins. If you do agentic work with big context windows, this single threshold tells you which API to default to.

Concrete daily-spend math at 1M input + 1M output tokens per day:

That's a ~18.5x spread between DeepSeek's promo rate and GPT-5.5 Pro for the same nominal token volume. See our full AI coding tool pricing breakdown for IDE-bundled subscription comparisons.

Wildcard 1 — DeepSeek V4 promo cliff. u/BriefImplement9843 on r/LocalLLaMA: "until the 5th. nobody was using it at the old price." The $0.145/$1.74 pricing reverts to roughly 4x that on May 6. If you're planning a workload around V4-Pro economics, build in a switch to DeepSeek V3.2 or Qwen3-Max as the post-promo fallback.

Wildcard 2 — V4-Pro is verbose to a fault. u/AnomalyNexus: "It writes these huge walls of text about what it's doing. Like just solid blocks of just essays." u/look: "both per token price and its reasoning token use is off the charts." The headline cost saving is gross of model self-talk. Same caveat applies as the Opus 4.7 tokenizer footnote — benchmark your real prompts.

Wildcard 3 — V4-Pro hallucinations. u/look again: "it has an extremely high hallucination rate. It knows a lot of things, but when it doesn't know the answer, it makes something up." The pattern u/2Norn settled on after a week of testing: V4-Pro and V4-Flash as "worker subagents" for implementation only — no design, no planning, no research. Claude/GPT still handle the thinking layer.

Wildcard 4 — Gemini 3.1 Pro time-to-first-token. Artificial Analysis clocks Gemini 3.1 Pro at 21.14 seconds TTFT versus 111.9 output tok/s throughput. The high TTFT is a reasoning-overhead artifact. For chat UX it's noticeable; for batch jobs it doesn't matter.

Wildcard 5 — Opus 4.7's BrowseComp regression. Opus 4.7 dropped on web-research benchmarks vs Opus 4.6. Gemini 3.1 Pro's 85.9% BrowseComp is the right tool for browse-heavy research, not Opus.

If you're picking exactly one model for a small team, the honest answer is two models: Claude Opus 4.7 for ambitious code on large codebases, GPT-5.5 for terminal/agentic work and long-context jobs under 272K input. Use Gemini 3.1 Pro for research-style queries that benefit from BrowseComp and GPQA strength, and DeepSeek V4-Pro as a cheap implementation-layer subagent — but only until May 5, and only behind a thinking model that catches its hallucinations.

For a workflow-by-workflow walkthrough beyond just these four, see our task-by-task model picker guide. For deeper coverage of the open-weights side specifically, see the DeepSeek V4 deep-dive.