Claude Sonnet 5.5 vs GPT-5.6 Luna
Anthropic · US | OpenAI · US · Updated June 2026
Quick verdict
Pick Claude Sonnet 5.5 for terminal-bench 4.0 agentic coding jumps to 70.6%, up from claude sonnet 5's 10.3% or same $2/$10 per million token pricing as sonnet 5, with cache-read price cut 50% to $0.10/mtok as of october 7, 2026. Pick GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation or fast, affordable execution while keeping respectable coding. On a tight budget at scale, GPT-5.6 Luna is the value pick.
Claude Sonnet 5.5 (Anthropic) and GPT-5.6 Luna (OpenAI) are two of the models people most often weigh against each other in 2026. Claude Sonnet 5.5 is anthropic's September 28, 2026 mid-tier update — same $2/$10 pricing as Sonnet 5 but a large agentic-coding jump (Terminal-Bench 4.0: 70.6% vs 10.3%). GPT-5.6 Luna is the budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Price: GPT-5.6 Luna is about 2× cheaper on input ($1/$6 per 1M tokens vs $2/$10 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: both advertise 1M tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: Claude Sonnet 5.5 is the newer model by about 3 months (released September 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Claude Sonnet 5.5 | GPT-5.6 Luna |
|---|---|---|
| Provider | Anthropic (US) | OpenAI (US) |
| Released | September 28, 2026 | July 9, 2026 |
| Context window | 1M tokens (~1,500 pages) | 1M (~1,500 pages) |
| Price (in/out) | $2/$10 per 1M tokens | $1/$6 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Terminal-Bench 4.0 agentic coding jumps to 70.6%, up from Claude Sonnet 5's 10.3%
Claude Sonnet 5.5
Anthropic's September 28, 2026 mid-tier update — same $2/$10 pricing as Sonnet 5 but a large agentic-coding jump (Terminal-Bench 4.0: 70.6% vs 10.3%) — and it is the newer of the two.
Same $2/$10 per million token pricing as Sonnet 5, with cache-read price cut 50% to $0.10/MTok as of October 7, 2026
Claude Sonnet 5.5
Claude Sonnet 5.5 lists same $2/$10 per million token pricing as Sonnet 5, with cache-read price cut 50% to $0.10/MTok as of October 7, 2026 among its strengths; GPT-5.6 Luna does not.
Scores close to Opus 5.5 on several benchmarks (e.g. GDPval-AA v2.1 knowledge-work Elo) at a fraction of the cost
Claude Sonnet 5.5
Claude Sonnet 5.5 lists scores close to Opus 5.5 on several benchmarks (e.g. GDPval-AA v2.1 knowledge-work Elo) at a fraction of the cost among its strengths; GPT-5.6 Luna does not.
Cheapest GPT-5.6 tier for high-volume drafting and automation
GPT-5.6 Luna
At $1/$6 per 1M tokens it undercuts Claude Sonnet 5.5 ($2/$10 per 1M tokens), and that gap compounds at volume.
Fast, affordable execution while keeping respectable coding
GPT-5.6 Luna
The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning — and it runs cheaper at $1/$6 per 1M tokens.
Same 1M context and programmatic tool calling as its siblings
GPT-5.6 Luna
Claude Sonnet 5.5 is comparatively weak here — 1M-token context isn't uniformly available across every platform/plan, consistent with the rest of the 5.5 family
Lowest cost at scale
GPT-5.6 Luna
At $1/$6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-5.6 Luna
At $1/$6 per 1M tokens it undercuts Claude Sonnet 5.5, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is terminal-bench 4.0 agentic coding jumps to 70.6%, up from claude sonnet 5's 10.3%
→ Claude Sonnet 5.5
It is specifically built for that.
Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation
→ GPT-5.6 Luna
That is its strongest area.
Claude Sonnet 5.5: where it fits
Anthropic's September 28, 2026 mid-tier update — same $2/$10 pricing as Sonnet 5 but a large agentic-coding jump (Terminal-Bench 4.0: 70.6% vs 10.3%). Released September 28, 2026 by Anthropic, it is built for terminal-Bench 4.0 agentic coding jumps to 70.6%, up from Claude Sonnet 5's 10.3%, same $2/$10 per million token pricing as Sonnet 5, with cache-read price cut 50% to $0.10/MTok as of October 7, 2026, scores close to Opus 5.5 on several benchmarks (e.g. GDPval-AA v2.1 knowledge-work Elo) at a fraction of the cost, and anthropic highlights stronger document/slide generation, spreadsheet work, and visual chart recognition (Chartography: 61.6%).
Its trade-offs are real: anthropic is explicit that Opus 5.5 'remains clearly stronger' on complex, open-ended work requiring sustained judgment, 1M-token context isn't uniformly available across every platform/plan, consistent with the rest of the 5.5 family, and mid-tier update released a week after Opus 5.5 (Sept 22) — Haiku 5.5 followed Oct 7 to complete the family. At $2 in / $10 out per million tokens, it sits in the mid price band.
GPT-5.6 Luna: where it fits
The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. Released July 9, 2026 by OpenAI, it is built for cheapest GPT-5.6 tier for high-volume drafting and automation, fast, affordable execution while keeping respectable coding, same 1M context and programmatic tool calling as its siblings, and high-throughput simple agentic jobs.
Its trade-offs: weak long-context recall deep in its 1M window (MRCR far below Sol), and lowest raw capability of the three GPT-5.6 tiers; no open weights. At $1 in / $6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Claude Sonnet 5.5 and GPT-5.6 Luna overlap enough that the right pick depends on your specific job. GPT-5.6 Luna costs less per token; and each leads in its own area — Claude Sonnet 5.5 for terminal-bench 4.0 agentic coding jumps to 70.6%, up from claude sonnet 5's 10.3%, GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Claude Sonnet 5.5 and GPT-5.6 Luna without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Claude Sonnet 5.5 or GPT-5.6 Luna better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Claude Sonnet 5.5 leans toward terminal-bench 4.0 agentic coding jumps to 70.6%, up from claude sonnet 5's 10.3% while GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Claude Sonnet 5.5 or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper — $2/$10 per 1M tokens vs $1/$6 per 1M tokens, roughly 2× apart on input.
Which has the bigger context window?
Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Claude Sonnet 5.5 and GPT-5.6 Luna together?
Yes — a multi-model platform like LumiChats gives you Claude Sonnet 5.5, GPT-5.6 Luna and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Claude Sonnet 5.5 or GPT-5.6 Luna?
Claude Sonnet 5.5 — released September 28, 2026, about 3 months after GPT-5.6 Luna.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.