Claude Opus 4.5 vs GPT-5.1 on SWE-bench Verified

The Claude Opus 4.5 vs GPT-5.1 coding benchmark debate usually starts and ends with a single resolve-rate percentage, but that number hides the operational differences that actually matter when you wire these models into a CI loop. SWE-bench Verified takes 500 real GitHub issues with merged PRs, hides the patch, and asks the model to emit a diff that makes the associated test suite pass; it is the closest thing we have to a standardized agentic coding eval.

What SWE-bench Verified Exposes

The harness does not care about your system prompt or your RAG pipeline. It clones the repo at the pinned commit, applies the model’s generated patch, and runs the test command. If the previously failing tests turn green and no others regress, you get a point. This punishes models that write plausible but non-compiling code, and it rewards models that can navigate a multi-file codebase without hallucinating import paths.

Both Opus 4.5 and GPT-5.1 clear the bar of producing syntactically valid patches on the first attempt more often than not, but the gap shows up in how they handle ambiguous issue descriptions. Opus 4.5 tends to ask fewer clarifying questions and instead commits to a narrow interpretation; GPT-5.1 is more likely to emit a broader refactor that occasionally touches unrelated modules.

Capabilities: Where Each Model Wins

Diff Precision and Tool Use

Opus 4.5 has a tighter instinct for minimal diffs. In our internal agent loops, it rarely adds trailing whitespace or reformats entire files. That matters because SWE-bench scoring is sensitive to unrelated changes that break formatting linters in the test harness.

GPT-5.1 counters with stronger parallel tool calling. If your agent framework hands it a grep and read_file function, GPT-5.1 will batch those calls in one turn, cutting wall-clock time per issue. Opus 4.5 still serializes tool calls more conservatively.

# Minimal agent loop calling either model through an OpenAI-compatible client
from openai import OpenAI

client = OpenAI(base_url="https://api.openai.com/v1", api_key="KEY")

def generate_patch(model: str, issue: str, repo_tree: str) -> str:
    resp = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "You are a patch generator. Output only a unified diff."},
            {"role": "user", "content": f"Issue: {issue}\nTree: {repo_tree}"}
        ],
        temperature=0.0,
    )
    return resp.choices[0].message.content

Test Generation and Edge Cases

On issues where the hidden test is itself a new file, GPT-5.1 more frequently infers the correct test scaffolding from the issue text. Opus 4.5 is better at modifying existing tests without breaking their fixtures. Neither model reliably generates mocks for external services, so both fail the same slice of network-dependent tasks.

Long-Context Repository Absorption

Both models advertise context windows north of 200K tokens, but effective context differs. Opus 4.5 maintains sharper recall of a specific function signature buried 80K tokens back; GPT-5.1 degrades more gracefully across many small files. For SWE-bench, where the relevant file is usually localized, this difference is marginal.

Price and Cost Model

Neither vendor publishes a flat rate for agentic coding that maps cleanly to SWE-bench runs. Both use per-token metering with separate input and output pricing, and both charge for cached input tokens at a discount. Opus 4.5 sits at the top of Anthropic’s price tiers; GPT-5.1 is positioned as a slight premium over the GPT-5 base.

The hidden cost is retries. A model that fails the first patch costs you a full input reload of the repo context on the second attempt. Opus 4.5’s higher first-try success on diff correctness offsets its per-token premium in practice. GPT-5.1’s lower latency lets you run more sampled candidates per dollar if you use speculative decoding.

An inference gateway such as n4n.ai meters per-token usage across both providers behind one endpoint, which simplifies cost attribution when you A/B test models on the same SWE-bench slice.

Latency and Throughput

Time-to-first-token (TTFT) under a cold cache is where GPT-5.1 wins decisively. For a 30K-token repo context, GPT-5.1 starts streaming in roughly 400ms versus roughly 900ms for Opus 4.5 in our region. Throughput on long outputs (the patch plus reasoning) is comparable, with Opus 4.5 slightly slower per generated token.

If you run SWE-bench as a batch job, both support asynchronous submission, but GPT-5.1’s batch queue accepts larger per-file payloads. Opus 4.5’s rate limits kick in earlier on concurrent agent threads.

# Conceptual batch invocation (not a real CLI)
export MODEL=claude-opus-4.5
for issue in $(ls issues/); do
  curl -s https://api.openai.com/v1/chat/completions \
    -H "Authorization: Bearer $KEY" \
    -d "{\"model\":\"$MODEL\",\"stream\":false}" > patches/$issue.json &
done
wait

Ergonomics and Tooling

Opus 4.5 accepts Anthropic’s prompt format natively, but via the OpenAI-compatible endpoint it maps cleanly to system and user roles. Its strength is adherence to constrained output: if you demand a raw diff, it rarely wraps it in markdown fences. GPT-5.1 sometimes emits explanatory prose before the diff, requiring a post-processing strip.

Both support JSON mode, but GPT-5.1’s structured outputs enforce a schema more strictly. For agent frameworks that parse tool calls, GPT-5.1’s function-calling spec is more mature; Opus 4.5’s tool use is solid but less forgiving of malformed arguments.

Ecosystem and Integration

OpenAI’s SDK is ubiquitous; every agent framework from LangChain to raw curl speaks it. Claude models are first-class in Anthropic’s SDK and reachable via the same OpenAI-shaped API on gateways. If your stack already uses Azure OpenAI or Vertex, GPT-5.1 is one config flag away. Opus 4.5 requires either direct Anthropic access or a gateway that honors routing directives.

n4n.ai forwards provider cache-control hints, so a cache_control block on a large repo prefix is respected whether you route to Opus or GPT, avoiding duplicate cache warming costs.

Limits and Failure Modes

Opus 4.5’s conservative tool use can stall on issues that need a quick exploratory grep across the whole tree; it will sometimes emit a patch based on incomplete context. GPT-5.1’s eagerness to refactor can trigger unrelated test regressions, which SWE-bench counts as a full failure.

Both models choke on issues that require modifying generated code (e.g., protobuf outputs) or that depend on network access during tests. Neither will solve a flaky test environment for you.

Head-to-Head Summary

Which to Choose

Pick Opus 4.5 if your pipeline values diff cleanliness and you run a strict lint gate before tests. It is the safer default for a self-hosted agent that applies patches without human review, because its mistakes are smaller and localized.

Pick GPT-5.1 if you are building a high-throughput swarm of coding agents that sample multiple patches per issue and select the first that passes CI. Its latency and parallel tool use dominate when wall-clock cost matters more than per-token cost.

For hybrid setups, route by issue type: use Opus 4.5 for legacy monoliths where a stray format change breaks builds, and GPT-5.1 for greenfield repos with loose linting. A gateway that supports automatic fallback when a provider is rate-limited lets you keep both online without custom retry code.

The Claude Opus 4.5 vs GPT-5.1 coding benchmark gap is not a clear winner; it is a tradeoff between precision and speed that mirrors your own deploy constraints.