← News
RELEASE · 2026-04-09
Claude Opus 4.7 vs GPT-5.5 vs Gemini 3.5: Frontier Model Showdown
Opus 4.7 leads SWE-Bench Verified at 87.6%. GPT-5.5 wins Terminal-Bench 2.0 at 82.7% and long-context reasoning. Gemini 3.5 Flash undercuts both on price while holding most of a 1M context.
The 2026 frontier line-up at a glance
Three labs shipped near-simultaneously this spring. Anthropic released Claude Opus 4.7 on April 16, 2026, framing it as a software-engineering upgrade over Opus 4.6 with stronger long-horizon task discipline. OpenAI followed on April 23 with GPT-5.5, positioning it around agentic computer use, then pushed GPT-5.5 Instant to free ChatGPT on May 5. Google rounded out the cycle at I/O on May 19 with Gemini 3.5 Flash, with a 3.5 Pro variant flagged for June.
A few things differentiate this round from prior cycles:
- Anthropic publicly conceded that an unreleased internal model (Mythos) outperforms Opus 4.7, framing the release as the safer shipping option.
- OpenAI roughly doubled per-token pricing on the GPT-5 line at the 5.5 cutover.
- Google leaned harder on price-per-token than headline benchmark wins, releasing Flash before Pro.
All three position themselves primarily as agent platforms rather than chat models.
Coding: where Claude Opus 4.7 currently leads
On SWE-Bench Verified, Opus 4.7 reports 87.6% — up from 80.8% on Opus 4.6 — and 64.3% on SWE-Bench Pro, a 10.9-point jump generation-over-generation. Independent comparisons consistently put it ahead of GPT-5.4 and Gemini 3.1 Pro on the harder Pro split, which is the better proxy for messy real-world repository work.
The practical character of this lead matches the benchmark gap. Opus 4.7 tends to produce more thorough multi-step edits, verify its own diffs against test output before reporting completion, and refactor across files without losing the plot. The cost is verbosity: comparative runs show Opus producing roughly 3.5x the output tokens GPT-5.5 uses for the same coding task, which matters once you multiply by daily agent runs.
If your loop is plan, edit, run tests, repeat — across a non-trivial codebase — Opus 4.7 is the current default to beat.
Agentic terminal work: GPT-5.5's strengths
GPT-5.5 wins where the work is shell-shaped rather than diff-shaped. OpenAI reports 82.7% on Terminal-Bench 2.0 against Opus 4.7's 69.4%, and a similar gap appears on math-heavy reasoning suites — 35.4% vs 22.9% on FrontierMath Tier 4. Long-horizon computer-use tasks, browser automation, and tool-mediated debugging are where the gap is widest in independent testing.
The model's other notable property is token economy. On matched coding evaluations, GPT-5.5 produces about 72% fewer output tokens than Opus 4.7 to reach a similar outcome. That partially offsets the higher list price on output tokens. The trade-off is style: GPT-5.5's edits are terser and assume more context awareness from the orchestrator, which works well inside Codex-style harnesses but can underspecify when driving a less structured agent loop. Pick it for terminal-native agents and validation-heavy workflows.
Speed and context: Gemini 3.5 Flash and the 1M-token reality check
Gemini 3.5 Flash ships with a 1,048,576-token input window and 65,536-token output ceiling. Google reports it outperforming Gemini 3.1 Pro on coding and agentic suites at roughly 4x the speed, with requests that took 8-10 seconds on 3.1 Pro landing in 2-3 seconds. On long-context retention specifically, 3.5 Flash gives back about 7.6 points to 3.1 Pro at 128k but closes to within 0.3 points at the full 1M.
Real deployments are already public — Macquarie Bank for 100-plus-page onboarding documents, Ramp for messy invoice OCR — and the use case is generally the same: feed the entire artifact, skip the retrieval pipeline. Flash is not the strongest reasoner in this group, but it is the only one of the three that makes whole-codebase or whole-document context economically routine. The 3.5 Pro variant, expected in June, may close the reasoning gap with the others.
Pricing per million tokens, side by side
Prices below are list standard-tier, USD per million tokens, as checked on May 27, 2026.
- Claude Opus 4.7: $5 input / $25 output (unchanged from Opus 4.6)
- GPT-5.5: $5 input / $30 output (doubled from GPT-5's $2.50 / $15 at the 5.5 cutover)
- GPT-5.5 Pro: $30 input / $180 output
- Gemini 3.5 Flash: $1.50 input / $9 output (cached input $0.15)
Flex and Batch tiers cut GPT-5.5 to $2.50 / $15. Priority routing raises it to $12.50 / $75. Prompt caching is meaningful across all three — Anthropic and OpenAI both publish discounted cached-input rates, and Gemini's $0.15 cached input is the lowest on the list. For a typical agent loop with heavy prompt reuse, effective cost can be a third to half of headline list. Output token volume is where Opus 4.7's verbosity costs you, and where GPT-5.5's terseness partially earns back its price premium.
When to route across all three instead of picking one
The honest read on the 2026 frontier is that no single model dominates. Opus 4.7 leads roughly 6 of 10 shared public benchmarks against GPT-5.5; GPT-5.5 leads the other 4, mostly math and terminal work. Gemini 3.5 Flash wins on cost and context. Picking one as a hard default leaves capability on the table on every task that doesn't match its shape.
A pragmatic pattern in production agent stacks is per-role pinning: Opus for code edits, GPT-5.5 for terminal and validation loops, Gemini 3.5 Flash for retrieval-free long-context summarization and cheap pre-processing. This is what platforms like osFoundry already do with built-in fallback chains and BYOK pure-passthrough billing — one router, three providers, no per-seat markup. The architectural commitment is fallback handling and prompt-format normalization, which is a one-time engineering cost that pays back the first time one provider's API has a bad afternoon.
Migration checklist if you're leaving a single-vendor stack
Going multi-model is not just an API swap. A short pre-flight list keeps the migration cheap:
- Normalize tool-call schemas. Anthropic, OpenAI, and Google use materially different JSON shapes; the cheapest abstraction is your own adapter layer rather than depending on any one SDK's translation.
- Pin per-role models in config, not in code. You will re-pin within a quarter.
- Re-baseline cost using your real prompt mix, including cache hit rate, not the list per-million numbers.
- Re-evaluate at least three of your hardest production prompts on each candidate. Public benchmarks are directional, not predictive of your workload.
- Wire fallback chains before you flip traffic. The point of multi-model isn't price arbitrage, it's surviving the next provider outage.
Do this once, and the cycle that ships GPT-5.6 or Opus 4.8 becomes a config change rather than a quarter of engineering.
Frequently asked questions
- Which is the best frontier LLM for coding in 2026?
- On the published 2026 benchmarks, Claude Opus 4.7 leads SWE-Bench Verified at 87.6% and SWE-Bench Pro at 64.3%, with the Pro split being the better proxy for real repository work. GPT-5.5 wins on terminal-driven and validation-heavy agent loops, scoring 82.7% on Terminal-Bench 2.0. Gemini 3.5 Flash is the cheap option for whole-codebase context. The honest answer is that no single model dominates every coding shape, and the best choice depends on whether your loop is diff-shaped, shell-shaped, or context-shaped.
- Is GPT-5.5 cheaper than Claude Opus 4.7?
- Not on list price. As of late May 2026, both charge $5 per million input tokens on the standard tier, but GPT-5.5 charges $30 per million output tokens versus $25 for Opus 4.7. GPT-5.5 partially offsets this by producing roughly 72% fewer output tokens on matched coding tasks, which can flip the effective cost in its favor for terse, structured workloads. On Flex or Batch tiers, GPT-5.5 drops to $2.50 / $15 per million, making it materially cheaper than Opus 4.7 for offline jobs.
- Can Gemini 3.5 Flash really use its full 1 million token context?
- Mostly yes, with caveats. Google's published evaluations show 3.5 Flash giving back about 7.6 points to Gemini 3.1 Pro at 128k context, then closing to within 0.3 points at the full 1M, which is unusually flat for long-context degradation. Public deployments at Macquarie Bank and Ramp confirm the window is usable end-to-end on 100-plus-page documents. The model is not the strongest pure reasoner in the frontier set, but it is the only one that makes feeding entire codebases or document corpora economically routine.
- Should I switch from a single model provider to multi-model routing?
- If your agent workload spans coding, terminal work, and long-context retrieval, yes. No 2026 frontier model wins all three categories, and the per-task gaps are large enough to matter at production scale. The engineering cost is real but bounded: a tool-call schema adapter, per-role model pinning in config, and a fallback chain. Once that infrastructure exists, swapping in the next generation of any vendor becomes a config change. The other win is resilience — multi-model routing survives any single provider's outage.
Sources