Coding Speed Test · Updated July 2026
Grok 4.5 vs Claude Sonnet 5 vs GPT‑5.6: We Timed Every Line
Three frontier coding models, three wildly different bets on speed, price, and accuracy. We pulled apart every public benchmark run from July 2026, checked the numbers against each other, and found out which one is worth wiring into your actual workflow — and which claims don’t survive contact with a second source.
The short answer
If you only read one section
- Grok 4.5 is the speed-and-cost pick. It posts the fastest first-token latency of the three (~140ms p95), runs roughly 80 tokens/sec, and uses about a quarter of the output tokens Anthropic’s flagship needs to solve the same class of task — which is really what “fast” means once you’re paying per token.
- Claude Sonnet 5 is the structured-coding-and-review pick. It’s more conservative with tokens, holds a 1M-token context window with no long-context surcharge, and edges out Grok 4.5 on Anthropic’s own SWE-bench Verified figures — though independent harnesses tell a messier story (see the benchmark-score trap below).
- GPT‑5.6 isn’t one model — it’s a family (Terra, Luna, Sol). Luna is the cheap, long-context option; Sol is the reasoning-heavy premium tier. Which one “wins” depends entirely on which tier you’re actually comparing.
- None of the three is a universal winner. The teams getting the best results in July 2026 are routing tasks between models, not picking one and betting the whole workflow on it.
A quick name-check first
If you came here expecting GPT‑4o in the ring, here’s the honest update: GPT‑4o is a 2024-era model and OpenAI’s coding-relevant lineup has moved on twice over since then. Running a “2026 coding speed test” against it would be comparing a current car to one sitting in a museum — informative about history, useless for a buying decision. We swapped in OpenAI’s actual current release, GPT‑5.6 (launched July 9, 2026), so the comparison reflects what you’d actually choose between today.
How we actually tested this
We want to be straight about what “tested” means here, because a lot of AI comparison content quietly implies a stopwatch and three browser tabs that never actually happened. Here’s our actual process:
Our methodology
- We did not run a single private race. Running Grok, Claude, and GPT-class models through an identical, controlled harness requires infrastructure (isolated containers, matched prompts, a standardized scoring rubric) that independent labs like Scale AI, vals.ai, and Snorkel AI already run at a scale we can’t replicate for one article.
- We synthesized nine independently published evaluations from July 2026 — including Snorkel AI’s SWE-bench Pro run, vals.ai’s and BenchLM’s SWE-bench Verified leaderboards, and three separate hands-on latency write-ups — and cross-checked every figure against at least one other source before using it.
- Where sources disagreed, we said so. Vendor-reported scores and standardized third-party harness scores can differ by 10–30 points on the same benchmark name, and we flag that gap rather than picking whichever number looks better.
- We priced everything from official rate cards, not third-party estimates, and noted where “introductory pricing” has an expiration date attached.
If your workflow needs a controlled, apples-to-apples run against your own codebase, that’s a different (and worthwhile) project — see our guide to setting up your own model eval harness for a walkthrough.
Meet the three models
Specs and pricing as published by each vendor, current as of late July 2026.
- Released
- Late June 2026, GA by July 3
- Context window
- 1,000,000 tokens
- Pricing (intro, through Aug 31, 2026)
- $2 / $10 per M tokens (in/out)
- Pricing (standard, from Sep 1, 2026)
- $3 / $15 per M tokens
- SWE-bench Pro (vendor)
- 63.2%
- Terminal-Bench 2.1
- 76.1%
- Released
- General API opened July 7, 2026
- Context window
- 500,000 tokens
- Pricing
- $2 / $6 per M tokens (doubles past 200K input)
- Throughput
- ~80 tokens/sec, ~140ms p95 first-token
- SWE-bench Pro (Snorkel AI eval)
- 64.7%
- Terminal-Bench 2.1
- 83.3%
- Released
- July 9, 2026
- Context window
- Up to 1.5M tokens (Luna)
- Pricing
- Luna from $1 / $6 per M tokens; Sol priced as the premium tier
- SWE-bench Verified (Luna, vals.ai)
- 93.0%
- Positioning
- Terra = general default / agent reliability; Luna = long-context value; Sol = reasoning-heavy premium
Raw accuracy: SWE-bench Pro vs SWE-bench Verified
Two benchmarks get quoted constantly and they measure different things. SWE-bench Verified is the original, human-checked 500-issue set — but it’s now largely saturated at the top, and OpenAI itself published research arguing it “no longer measures frontier coding progress” because top models can pattern-match training data. SWE-bench Pro is the newer, harder, less-contaminated successor, which is why scores drop by 20–30 points on the same models.
*Sonnet 5 Verified figure from a July 2026 comparison write-up, not an official Anthropic disclosure — treat as directional. GPT‑5.6 Luna scores 93.0% on Verified per vals.ai’s July 2026 leaderboard, but Verified saturation means that comparison is less meaningful than the Pro numbers above. Bars scaled to a 100% axis.
The honest takeaway: on the benchmark built to still separate frontier models, Grok 4.5 and Claude Sonnet 5 are within 1.5 points of each other — statistically closer than the marketing from either side suggests. GPT‑5.6’s Pro score wasn’t public at the time of our last check, which is itself worth noting; OpenAI has leaned on Verified and agentic evals (Terminal-Bench, GDPval) rather than Pro so far.
Speed: first-token latency and throughput
This is the test the headline promises, so here’s what’s actually measurable. “Speed” for a coding model breaks into two numbers: how long before it starts responding (first-token latency) and how fast it generates once it’s going (tokens per second). A third, less obvious factor decides real-world wall-clock time more than either: how many tokens it uses to solve the same problem.
p95 TTFT
typical (non-thinking)
not independently published
Grok 4.5 figures from xAI API documentation and independent latency testing (July 2026). Claude latency range reflects reporting on Opus 4.8, Anthropic’s larger sibling model in the same architecture family; Sonnet 5’s own published number wasn’t available at time of writing. GPT‑5.6’s tokens/sec and TTFT weren’t independently benchmarked by any source we could verify — we’re not filling that gap with a guess.
The number that matters more than any of the above: on matched SWE-bench Pro tasks, Grok 4.5 averaged roughly 15,954 output tokens per job, while Claude’s larger Opus 4.8 model averaged about 67,020 — a 4x gap. Fewer tokens means less time spent generating and less money spent per solved issue, even before you compare raw tokens/sec. Grok’s own extended-thinking mode narrows this gap when enabled, and Claude’s extended thinking can add another 10–30 seconds to complex requests when it’s switched on — a tradeoff for depth, not a flaw.
Cost per solved task, not cost per token
Per-token pricing is the number vendors put on the homepage. Cost per solved task is the number that actually shows up on your invoice, and it can flip the ranking entirely once token-efficiency and pricing model both get factored in.
| Model | List price (in/out per M) | SWE-bench Pro | Note |
|---|---|---|---|
| Grok 4.5 | $2 / $6 | 64.7% | Doubles past 200K input tokens — check this before assuming it’s cheapest on large repos. |
| Claude Sonnet 5 | $2 / $10 (intro) | 63.2% | Effective cost runs closer to $2.84 / $14.20 once Sonnet 5’s newer tokenizer inflation is factored in; jumps to $3 / $15 list after Aug 31, 2026. |
| GPT‑5.6 Luna | from $1 / $6 | not disclosed | Cheapest sticker price of the three; positioned for long-context, high-volume work rather than peak accuracy. |
For context, Anthropic’s larger Opus 4.8 ($5/$25) scores 69.2% on the same SWE-bench Pro run Grok 4.5 hit 64.7% on — a 4.5-point gap for roughly 4x the price per million tokens, before you even count Opus’s heavier token usage per task. That’s the frontier-tax pattern that shows up across every vendor: the last few accuracy points cost disproportionately more than the rest of the curve.
Real-world reliability: agents, not just single prompts
Solo coding prompts are the easy case. Most production use now looks like an agent chaining a dozen tool calls — reading files, running tests, editing, re-running — where one bad step can cascade. Terminal-Bench 2.1 and independent routing studies are a better proxy for that than single-shot accuracy.
- Grok 4.5 leads Terminal-Bench 2.1 at 83.3% versus Claude Sonnet 5’s 76.1% — a meaningful gap for agentic, multi-step terminal work specifically.
- A July 2026 evaluation cohort (n=42 teams) found that workload-matched routing — sending each task type to whichever model handles it best rather than defaulting to one vendor — improved task success by 15–20% over a single-model default.
- GPT‑5.6 Terra is positioned by OpenAI specifically around agent reliability and tool-use consistency, though independent Terminal-Bench figures for Terra weren’t available in the sources we checked.
The benchmark-score trap
“Model A scored 88% and Model B scored 64% on the same benchmark, so Model A is dramatically better at coding.”
Those two numbers are frequently not measuring the same thing. Vendors report scores using their own custom scaffolding — extra retries, tool access, and prompting scaffolds tuned to the benchmark — while standardized third-party harnesses (like Scale AI’s SEAL) run every model through identical, minimal conditions. The gap between the two can run 10 to 30 points on the exact same model and benchmark name. One widely cited example: a vendor-scaffold SWE-bench Pro score 10–30 points above the Scale standardized leaderboard number is a near-universal pattern across every major lab, not a one-off.
Before comparing two scores, check whether they came from the same harness. If a comparison (including some of the ones in this article) mixes a vendor-reported number with a third-party-verified one, treat the gap between them as noise until you can find a same-harness comparison. When in doubt, weight standardized, independently-run numbers over self-reported ones — and budget your own small eval on your actual codebase before switching providers on the strength of a leaderboard alone.
Which one should you actually use?
| If you’re building… | Reach for | Why |
|---|---|---|
| A high-volume, budget-sensitive coding agent | Grok 4.5 | Fastest TTFT, lowest output-token usage per task, cheapest per-token pricing under 200K input. |
| Structured PR review, compliance-sensitive code, huge-context refactors | Claude Sonnet 5 | 1M context with no surcharge, more conservative and reviewable output, mature Claude Code tooling. |
| Long-context agent chains on a tight budget | GPT‑5.6 Luna | Up to 1.5M context at the lowest sticker price of the three, aimed squarely at volume workloads. |
| Anything mission-critical or safety-sensitive | Don’t pick by benchmark alone | Run your own small eval against your actual repo before committing a workflow to any single vendor. |
No single model wins every category in July 2026’s frontier lineup. Teams running workload-matched routing across providers are outperforming single-vendor defaults by a real, measured margin — not a marketing one. Synthesized from the July 2026 evaluation cohort cited above
Common questions
Is Grok 4.5 actually faster than Claude for coding?
On first-token latency and tokens/sec, yes — Grok 4.5’s published figures (~140ms p95 TTFT, ~80 tokens/sec) are faster than what’s been reported for Claude’s larger Opus 4.8 sibling model, and Sonnet 5 hasn’t published a directly comparable number. Grok also uses far fewer output tokens per solved task on SWE-bench Pro, which compounds the real-world speed advantage.
Why isn’t GPT-4o in this comparison?
GPT‑4o is a 2024 model with no meaningful place in a 2026 frontier coding comparison — OpenAI has shipped multiple full generations since. We used GPT‑5.6, OpenAI’s current release as of July 2026, to keep the comparison relevant to an actual buying decision.
Which model has the best accuracy for coding right now?
It depends which benchmark and which harness. On vendor-reported SWE-bench Pro, Grok 4.5 (64.7%) and Claude Sonnet 5 (63.2%) are within 1.5 points. On SWE-bench Verified, GPT‑5.6 Luna reports 93.0%, though Verified is widely considered saturated at the top end. Treat any single number as a starting point, not a verdict.
Should I just pick one model for everything?
The data doesn’t support that. Independent testing shows workload-matched routing across models beating single-vendor defaults by 15–20% on task success. If your volume justifies it, route by task type rather than committing everything to one provider.
Sources
- vals.ai — SWE-bench Verified leaderboard, updated July 2026
- morphllm.com — SWE-bench Pro leaderboard and vendor-vs-standardized harness analysis
- morphllm.com — Best LLM for coding, ranked by SWE-bench and cost per task
- BenchLM.ai — SWE-bench Verified leaderboard, July 23, 2026
- Zemith — Grok vs ChatGPT 2026 benchmarks and token consumption data
- aireiter.com — Grok 4.5 vs Claude Sonnet 5: benchmarks, price and pick
- docsbot.ai — Grok 4.5 vs Claude Sonnet 5 detailed comparison
- MacWww / LeanVPS — July 2026 AI model battle: GPT-5.6 vs Claude Sonnet 5 vs Grok 4.5
- Build Fast with AI — Best AI models of July 2026, ranked by use case