Grok 4.3 vs GPT-5.5 on SWE-Bench Pro: Coding Benchmarks Tested (June 2026)

Grok 4.3 and GPT-5.5 are the two frontier models that get evaluated head to head most often in coding agent procurement conversations, because they bracket the budget versus premium tradeoff better than any other pairing on the market in mid 2026. GPT-5.5 leads SWE-Bench Pro by 6.4 percentage points and Aider Polyglot by 4.1. Grok 4.3 is half the input price, less than half the output price, and roughly 2x the throughput. The question is whether GPT-5.5's accuracy advantage compounds into enough task success rate improvement to justify the cost difference at production scale.

We ran both models on SWE-Bench Pro, Aider Polyglot, and a real coding agent workload over the past two weeks. This is the head to head with specific numbers.

Headline Comparison

Dimension	Grok 4.3	GPT-5.5
Released	May 2026	April 2026
Context window	512K tokens	256K tokens
Input price (per 1M)	$2.50	$5.00
Output price (per 1M)	$12.00	$30.00
SWE-Bench Pro	71.8%	78.2%
Aider Polyglot	73.6%	77.7%
Terminal-Bench 2.0	76.4%	82.7%
GPQA Diamond	68.5%	74.9%
Output tokens/sec (median)	112	58
Time to first token (p50)	0.9s	1.7s
Tool use reliability (internal eval)	87.8%	93.4%
Native prompt caching discount	75%	90%
Cost per SWE-Bench Pro task (median)	$0.34	$0.91

The headline reads: GPT-5.5 wins every accuracy benchmark by 4 to 6 percentage points. Grok 4.3 wins on price (2x cheaper input, 2.5x cheaper output), throughput (1.9x faster decode), and time to first token. The cost per task gap (2.7x) is wider than the price per million token gap because GPT-5.5 tends to produce longer reasoning traces on hard tasks. At a fleet level, that is the number that drives the procurement decision.

SWE-Bench Pro: Where GPT-5.5 Earns the Premium

SWE-Bench Pro is the harder verified subset of SWE-Bench Verified, with multi file edit requirements and broader test coverage. GPT-5.5 scored 78.2 percent in our run (OpenAI reports 79.1 percent, within margin). Grok 4.3 scored 71.8 percent (xAI reports 72.4 percent).

The 6.4 percentage point gap is informative but the failure mode breakdown matters more for selecting between them. GPT-5.5 fails primarily on tasks requiring novel algorithm design from scratch, where the model attempts a solution that compiles and passes the obvious unit tests but misses an edge case in the hidden tests. Grok 4.3 fails on a different class: multi file refactors where it loses track of which files it has already edited, similar to the failure mode we saw on Claude Fable 5 versus Grok 4.3 coverage.

For coding agent workloads on small to medium repositories (under 30 files in the working set), the SWE-Bench Pro gap narrows to roughly 3 percentage points. For monorepo workloads with cross service refactors, the gap widens to 9 percent. The shape of your codebase determines how much of the headline gap actually applies to your workload.

Aider Polyglot: Per Language Breakdown

Aider Polyglot tests code generation across Python, JavaScript, TypeScript, Rust, Go, C++, and Java. GPT-5.5 scored 77.7 percent. Grok 4.3 scored 73.6 percent.

Language	Grok 4.3	GPT-5.5
Python	78.9%	81.4%
TypeScript	76.2%	79.8%
Rust	64.1%	73.6%
Go	71.3%	76.4%
Java	73.5%	78.2%
C++	69.2%	74.8%
JavaScript	81.8%	82.6%

Two observations. First, Grok 4.3 and GPT-5.5 are within 1 percentage point on JavaScript, which is the language with the most training data and the smallest model differentiation. If your agent is mostly writing Node and frontend code, the accuracy delta is in the noise. Second, the Rust gap (9.5 percentage points) is the largest in the table; if you are shipping Rust services, GPT-5.5 is meaningfully more reliable. The C++ and Go gaps are smaller but still material.

Cost per Task on a Real Workload

We ran both models on an internal coding agent workload (216 tasks: code review comments converted to PRs against a Vue + TypeScript codebase, plus a smaller subset of Python data pipeline tasks). The metric is end to end task success rate plus median cost per completed task.

Metric	Grok 4.3	GPT-5.5
Task success rate	64.8%	71.3%
Median cost per successful task	$0.41	$1.18
p95 cost per task	$1.32	$3.84
Median wall clock per task	47s	92s
p95 wall clock per task	2m 14s	4m 38s

The success rate gap (6.5 percentage points) tracks the SWE-Bench Pro gap. The cost gap (2.9x at median) is wider than the per token pricing gap because GPT-5.5 emits longer reasoning traces on the harder tasks. Wall clock is roughly 2x in GPT-5.5's favor for Grok 4.3 because of the throughput advantage.

The economic interpretation: at this success rate distribution, Grok 4.3 costs $0.63 per task in the denominator that matters (cost per attempt, including failures, divided by success rate). GPT-5.5 costs $1.65. Grok 4.3 is 2.6x cheaper per shipped task even after accounting for its lower success rate.

That math reverses when failed tasks are expensive (production code in a domain where a bad PR has high cost). In those workloads, GPT-5.5's higher success rate is worth the price premium because the cost of a failed task is not just the API spend; it is the engineer time spent recovering from a bad change. The decision rule we use with clients: if a failed task costs less than $5 in engineer time to recover from, Grok 4.3 wins on economics. If a failed task costs more, GPT-5.5 starts to look right.

Tool Use Reliability

Coding agents do not just write code. They call file system tools, run tests, read diffs, and chain operations. Tool use reliability (the rate at which the model emits a syntactically valid tool call matching the schema, in the right place in the reasoning) is the metric that determines whether an agent workflow runs end to end or stalls.

GPT-5.5 hits 93.4 percent on our internal tool use eval (200 multi step tool calling scenarios across file ops, shell, web fetch, and search). Grok 4.3 hits 87.8 percent. The 5.6 percentage point gap compounds over multi step agent loops: a 5 step workflow with 93.4 percent per step reliability lands at 70.7 percent end to end. The same workflow at 87.8 percent lands at 52.0 percent. For long agent chains, GPT-5.5's tool use reliability advantage is bigger than the SWE-Bench gap suggests.

If your agent does long chains (10+ tool calls per task), GPT-5.5's reliability advantage dominates the procurement decision. If your agent is mostly single shot code generation with a final tool call, Grok 4.3's economics win.

Native Prompt Caching

Both models support native prompt caching. GPT-5.5 offers a 90 percent discount on cached input tokens (versus Anthropic's 90 percent on Claude). Grok 4.3 offers a 75 percent discount with a tighter TTL (5 minutes versus OpenAI's 60 minutes). For coding agents that reload the same large repository context across many tasks in a short window, both models cache effectively; for workloads with longer gaps between tasks, GPT-5.5's longer TTL and deeper discount matter.

On our agent workload with 200K token average context (a monorepo loaded once per session, then queried 8 to 20 times), the effective input cost after caching dropped from $5.00 per 1M to $0.50 for GPT-5.5 and from $2.50 to $0.62 for Grok 4.3. The cached cost gap narrows but Grok is still 20 percent cheaper.

Throughput and Latency

Grok 4.3's 112 tokens per second median decode is roughly 1.9x GPT-5.5's 58 tokens per second. Time to first token at p50 is 0.9 seconds for Grok 4.3 versus 1.7 for GPT-5.5. Both numbers matter for interactive coding agents where the user is waiting for the response.

For batch agent workloads (queued PR generation, background refactoring), throughput rarely matters; both models complete tasks in similar wall clock time when measured at p95 because the harder tasks dominate the runtime distribution and accuracy converges on long chains.

For interactive agents (a developer in the loop, waiting for the next suggestion), Grok 4.3's latency advantage is meaningful. The "feels fast" threshold is roughly 60 tokens per second sustained, and Grok 4.3 clears it comfortably while GPT-5.5 is right at the edge.

Decision Matrix

Workload	Pick
Interactive coding assistant, developer in the loop	Grok 4.3
Batch PR generation, success rate is the metric	GPT-5.5
Long agent chains (10+ tool calls per task)	GPT-5.5
High volume, low criticality (linting fixes, doc updates)	Grok 4.3
Rust or C++ heavy codebase	GPT-5.5
JavaScript or TypeScript monorepo, cost sensitive	Grok 4.3
Production code in regulated industry	GPT-5.5
Internal tooling, fast iteration	Grok 4.3

The honest summary is that Grok 4.3 is the right default for cost sensitive workloads where a 6 percentage point success rate hit is acceptable, and GPT-5.5 is the right default for workloads where accuracy compounds (long chains, multi file refactors, regulated codebases).

Working with Contra Collective

We build production AI coding agents for engineering teams. If you are picking between Grok 4.3 and GPT-5.5 for a coding agent integration, we run benchmarks against your actual codebase and task distribution rather than the published benchmarks. The decision often differs from the headline numbers once your specific failure mode profile is measured. Reach out via the Contra Collective contact page.

FAQ

Q: Should I default to Grok 4.3 or GPT-5.5 for a new coding agent project? A: Default to Grok 4.3 unless you have a specific reason to favor GPT-5.5 (long agent chains, Rust heavy stack, regulated code). The cost economics are too favorable to ignore at fleet scale.

Q: Does Grok 4.3's longer context window (512K vs 256K) matter for coding agents? A: For most codebases, no. The interesting context budget for a coding agent is 50K to 200K tokens, which both models handle comfortably. The 512K headroom matters for monorepo cases where you want to load multiple service boundaries at once.

Q: What is the practical difference in latency for an interactive agent? A: Grok 4.3 feels noticeably faster in a chat interface; first token in under 1 second and 112 tokens per second sustained. GPT-5.5 is acceptable for interactive use but the longer time to first token shows up in user perception.

Q: How do these compare to Claude Fable 5 and Opus 4.8 for coding? A: Claude Fable 5 leads SWE-Bench Pro by roughly 1 percentage point over GPT-5.5 and 7 over Grok 4.3, at a price closer to GPT-5.5. Opus 4.8 leads on tool use reliability but is the most expensive of the four. See our Claude Fable 5 vs GPT-5.5 SWE-Bench Pro coverage for the head to head.

Q: Does Grok 4.3's lower tool use reliability disqualify it from agentic workflows? A: Not categorically. For workflows with under 5 tool calls per task, the 5.6 percentage point gap costs about 4 percent end to end success rate, which Grok 4.3's price advantage more than compensates for. For workflows with 10+ tool calls, the gap compounds and GPT-5.5 wins.