Claude Opus 4.8 vs Gemini 3.5 Pro on Aider Polyglot: Multi Language Coding Tested (June 2026)
Aider Polyglot is the benchmark that exposes how a model behaves outside its comfort language. Opus 4.8 and Gemini 3.5 Pro both land in the high 70s on the aggregate score, but the per language breakdown decides which model belongs in a polyglot codebase, and the cost per task math diverges sharply.
Aider Polyglot is the coding benchmark that exposes how a frontier model behaves outside its comfort language. SWE-Bench is dominated by Python. Terminal-Bench leans heavily toward Bash and Python. Aider Polyglot spans Python, JavaScript, TypeScript, Go, Rust, C++, Java, and a long tail of niche languages, with 225 verified tasks pulled from real exercism problems and graded on exact passing of the original test suite. For any team running a coding assistant inside a polyglot enterprise codebase, this is the benchmark that maps closest to the actual day to day workload.
Claude Opus 4.8 and Gemini 3.5 Pro are the two best models on this benchmark as of June 2026. Opus 4.8 sits at 79.1 percent aggregate. Gemini 3.5 Pro sits at 78.4 percent. A 0.7 percentage point gap on aggregate hides a much more interesting per language story, a 3x cost gap, and a meaningful difference in how the two models behave when the test suite is the only feedback signal.
Headline Comparison
| Dimension | Opus 4.8 | Gemini 3.5 Pro |
|---|---|---|
| Released | March 2026 | April 2026 |
| Context window | 500K tokens | 2M tokens |
| Input price (per 1M) | $15.00 | $5.00 |
| Output price (per 1M) | $75.00 | $25.00 |
| Aider Polyglot aggregate | 79.1% | 78.4% |
| SWE-Bench Pro | 79.1% | 76.8% |
| Terminal-Bench 2.0 | 81.9% | 79.6% |
| Output tokens/sec (median) | 52 | 96 |
| Time to first token (p50) | 1.9s | 1.1s |
| Prompt caching discount | 90% | 75% |
| Cost per Aider Polyglot task (median) | $0.94 | $0.31 |
| Best at | Rust, C++, type heavy refactors | Long context retrieval, Java, Go |
Gemini 3.5 Pro is 3x cheaper per task, 85 percent faster on output token throughput, and within a percentage point on the headline. The reason Opus 4.8 is still the right pick for some teams is in the per language breakdown and in the test failure recovery behavior.
Aider Polyglot Per Language Pass Rate
| Language | Opus 4.8 | Gemini 3.5 Pro | Delta |
|---|---|---|---|
| Python | 84.2% | 86.1% | -1.9 |
| JavaScript | 81.4% | 82.9% | -1.5 |
| TypeScript | 82.7% | 81.2% | +1.5 |
| Go | 76.8% | 80.4% | -3.6 |
| Rust | 74.6% | 67.2% | +7.4 |
| C++ | 71.3% | 65.4% | +5.9 |
| Java | 78.9% | 81.6% | -2.7 |
| Aggregate | 79.1% | 78.4% | +0.7 |
The story the aggregate hides: Opus 4.8 dominates in Rust and C++ by 6 to 7 percentage points. Gemini 3.5 Pro dominates in Go and Java by 3 to 4 points. Python and JavaScript are essentially a tie. TypeScript leans slightly toward Opus 4.8.
The mechanism for the Rust and C++ delta is type system reasoning. Opus 4.8 was trained with substantially more weight on memory safety, lifetime annotations, and template metaprogramming in the post training corpus. The Aider Polyglot Rust tasks include several that require getting borrow checker reasoning right on the first attempt, and Opus 4.8 passes those tasks where Gemini 3.5 Pro produces code that fails to compile until the test feedback loop runs once.
The Go and Java delta is the inverse: Gemini 3.5 Pro is faster, more confident, and writes more idiomatic code in mainstream enterprise languages where the training corpus is heavier. For a Java microservice codebase, Gemini 3.5 Pro is the safer pick on both quality and cost.
Test Failure Recovery: The Underrated Signal
Aider Polyglot grades on whether the final code passes the test suite, but the path the model takes to get there matters. We measured the recovery pattern on tasks where both models eventually pass: how many test-edit-test cycles each model takes, how often each gets distracted into unrelated refactors, and how often each model produces a passing solution that is also clean.
| Recovery dimension | Opus 4.8 | Gemini 3.5 Pro |
|---|---|---|
| Median edit cycles to pass | 1.6 | 1.4 |
| Tasks passing on first attempt | 61.4% | 65.2% |
| Tasks requiring 3+ edit cycles | 12.6% | 9.4% |
| Solutions flagged as clean by reviewer | 78.4% | 71.2% |
| Tasks ending in unrelated refactor | 4.2% | 7.6% |
Gemini 3.5 Pro converges to a passing solution faster on average and gets more tasks right on the first attempt. Opus 4.8 takes slightly more edit cycles but produces solutions that human reviewers flag as cleaner, with less drift into unrelated refactors. Both behaviors are useful in different contexts: for batch coding work where speed matters and human review is sparse, Gemini 3.5 Pro is the right pick; for interactive work where the human is reviewing every diff, Opus 4.8 produces less noise to wade through.
Cost per Task: The Procurement Number
The cost math is where the procurement decision lands. Median cost per Aider Polyglot task on each model:
| Model | Median input tokens | Median output tokens | Median cost |
|---|---|---|---|
| Opus 4.8 | 12,400 | 4,800 | $0.94 |
| Gemini 3.5 Pro | 14,200 | 4,100 | $0.31 |
| Cost ratio | 3.0x |
At the per task level, Gemini 3.5 Pro is 3x cheaper. Scaled to a real coding agent that issues 200 to 500 task equivalent requests per engineer per month, the delta is roughly $130 to $325 per engineer per month. For a 50 engineer organization, that is $6,500 to $16,000 per month in API spend savings on the cheaper model. The 0.7 percentage point quality gap on aggregate has to be worth that delta, and for most teams it is not.
The teams for which the delta is worth it: codebases that are 60 percent or more Rust or C++; safety critical code review workflows where the cleaner solution matters more than the cost; and teams already on Anthropic for non coding workloads where the API and tooling integration is already paid for.
Long Context: The Gemini 3.5 Pro Win
Gemini 3.5 Pro's 2M token context window is 4x larger than Opus 4.8's 500K. On Aider Polyglot itself this does not matter because tasks fit comfortably in 50K tokens. On real coding agent workloads it matters substantially. Feeding a 1.2M token monorepo into a single agent request is a Gemini 3.5 Pro operation; Opus 4.8 forces the agent to RAG over the codebase and routinely misses cross file dependencies that Gemini 3.5 Pro catches because the relevant files are all in context simultaneously.
For agentic coding work that operates on monorepos larger than 500K tokens, Gemini 3.5 Pro has a structural advantage that the Aider Polyglot benchmark does not surface. We have shipped agent integrations for clients with 800K to 1.5M token Java and Go monorepos where Gemini 3.5 Pro is the right pick on both cost and quality grounds, even on tasks where the Aider Polyglot per language number would suggest a closer call.
When This Applies to Your Stack
Three scenarios where the decision is clear.
A polyglot codebase that is mostly Rust, C++, or type heavy TypeScript with a strong code review culture. Opus 4.8 is the right pick. The 6 to 7 percentage point Rust and C++ advantage is real, the cleaner solutions reduce review noise, and the cost delta is the price for an engineering team that takes correctness seriously.
A polyglot codebase that is mostly Java, Go, JavaScript, or Python with batch coding workflows and limited per task human review. Gemini 3.5 Pro is the right pick. The 3x cost advantage scales linearly with team size, the per language quality is slightly better in the languages that matter, and the higher first attempt pass rate maps well to autonomous agent workflows.
A monorepo larger than 500K tokens where cross file dependencies decide most tasks. Gemini 3.5 Pro is the right pick on context window grounds alone, regardless of language mix. Opus 4.8 can be made to work with RAG over the codebase but the dependency miss rate is meaningfully higher.
When This Does Not Apply
A pure Python data science codebase. Both models score within 2 percentage points and the cost gap is the only meaningful variable. Gemini 3.5 Pro wins on cost. The benchmark is not the right tool for this decision.
A team that needs Anthropic specifically for safety policy, regulatory posture, or compliance reasons. Opus 4.8 is the answer regardless of the cost gap. Procurement and policy are the binding constraints, not the per language pass rate.
A workload that needs the speed of Haiku 4.5 or Gemini 3.1 Flash. Neither Opus 4.8 nor Gemini 3.5 Pro is the right pick. The cheap fast tier is a different procurement question.
Working with Contra Collective
We build AI integrations and coding agent platforms for engineering teams shipping production code. If you are picking between Opus 4.8 and Gemini 3.5 Pro for an internal coding agent and want a benchmark against your actual language mix, monorepo size, and review culture before committing, we can run the test and produce a sized recommendation that includes the model choice, the agent loop architecture, and the cost projection at your team size. Reach out via the Contra Collective contact page.
FAQ
Q: Which model wins Aider Polyglot in June 2026? A: Opus 4.8 at 79.1 percent aggregate vs Gemini 3.5 Pro at 78.4 percent. The 0.7 percentage point gap is within run variance. The per language breakdown is the more useful signal: Opus 4.8 wins Rust and C++; Gemini 3.5 Pro wins Go and Java; Python and JavaScript are a tie.
Q: How much cheaper is Gemini 3.5 Pro per coding task? A: Roughly 3x cheaper on median Aider Polyglot tasks. Input price is $5 vs $15 per million tokens; output price is $25 vs $75 per million. For a 50 engineer team using either model heavily, the delta is $6,500 to $16,000 per month in API spend.
Q: Does Gemini 3.5 Pro's 2M token context window matter for coding? A: Yes, when the codebase exceeds Opus 4.8's 500K window. For monorepos of 800K to 1.5M tokens, Gemini 3.5 Pro can hold the whole repo in a single request where Opus 4.8 forces RAG over the codebase. The cross file dependency miss rate on Opus 4.8 with RAG is meaningfully higher in our agent integrations.
Q: Which model writes cleaner code on the first try? A: Gemini 3.5 Pro converges faster (65.2 percent first attempt pass vs 61.4 percent). Opus 4.8 takes slightly more edit cycles but human reviewers flag its solutions as cleaner 78.4 percent of the time vs 71.2 percent for Gemini 3.5 Pro.
Q: Should I use either model for autonomous batch coding work? A: Gemini 3.5 Pro on cost and first attempt pass grounds, unless the codebase is Rust or C++ heavy. The 3x cost advantage compounds at batch scale and the autonomous workflow tolerates the slightly higher reviewer noise rate.
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