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GPT-5.5 Price Increase: What It Actually Costs

GPT-5.5 Price Increase: What It Actually Costs

AI llmopenaipricing openrouter.ai
A cost analysis of GPT-5.5 reveals the 2x sticker price increase translates to actual cost increases of 49-92% depending on prompt length, with shorter outputs only benefiting longer prompts.
What: GPT-5.5 doubled its API pricing over GPT-5.4 ($2.50 to $5.00 per million input tokens, $15 to $30 per million output tokens), but OpenRouter's analysis of real-world usage shows the model generates 19-34% fewer tokens for prompts over 10K tokens, partially offsetting the increase.
Why it matters: Nominal pricing doesn't tell the full story: users with longer prompts see meaningful cost offsets from shorter completions (49-62% increase), while users with shorter prompts experience close to the full doubling (69-92% increase) with no verbosity benefit.
Takeaway: If you're using GPT-5.5 with prompts under 10K tokens, expect close to the full 2x cost increase; with longer prompts over 50K tokens, the actual increase drops to around 1.5x due to more concise outputs.
Deep dive
  • GPT-5.5's nominal pricing doubled from GPT-5.4: input tokens went from $2.50/M to $5.00/M and output tokens from $15/M to $30/M
  • OpenRouter analyzed real-world usage by tracking users who switched from GPT-5.4 to GPT-5.5 to measure actual cost impact
  • For prompts over 10K tokens, GPT-5.5 generates 19-34% fewer completion tokens, partially offsetting the price increase
  • For prompts under 10K tokens, completions stay the same length or get longer (up to 52% longer in the 2K-10K range)
  • Actual cost increases ranged from 49% (for 50K-128K token prompts) to 92% (for prompts under 2K tokens)
  • Users with longer prompts (over 10K tokens) saw cost increases of 49-62%, benefiting from shorter outputs
  • Users with shorter prompts (under 10K tokens) saw cost increases of 69-92%, getting little to no offset from verbosity reduction
  • The analysis used OpenRouter's independent token counting to provide a consistent baseline across model versions
  • Sample data came from April 21-23 for GPT-5.4 and April 25-28 for GPT-5.5, excluding the launch day itself
Decoder
  • GPT-5.4 / GPT-5.5: Sequential versions of OpenAI's GPT language model, with 5.5 being the latest release
  • OpenRouter: API aggregator that provides access to multiple LLM providers and tracks usage metrics
  • Token: Fundamental unit of text processed by LLMs, roughly 3-4 characters or 0.75 words
  • Input/Prompt tokens: Tokens in the request sent to the model
  • Output/Completion tokens: Tokens in the response generated by the model
  • Switcher cohort: Users who primarily used GPT-5.4 before the launch and switched to GPT-5.5 after
  • M: Million (as in $5.00/M = $5.00 per million tokens)
Original article

GPT-5.5 Price Increase: What It Actually Costs

We replicated the cost analysis we did on Opus on the new GPT-5.5 model. GPT-5.5 launched with a 2x price increase over GPT-5.4: input tokens increased from $2.50/M to $5.00/M and output tokens from $15/M to $30/M. OpenAI has also noted that the model is less verbose, producing shorter completions for the same tasks. Just as we did with Opus 4.7 we wanted to know what is the net impact on costs to users by analyzing usage that shifted from GPT-5.4 to GPT-5.5.

We observed cost increases between 49-92%. The price increase is mitigated by the model generating 19-34% fewer completion tokens for longer prompts.

Methodology: Same Switcher Cohort Approach

We used the same approach as our Opus 4.7 analysis. We identified users whose top model by request count was GPT-5.4 prior to the 5.5 launch, who then switched to GPT-5.5 as their top model. This "switcher cohort" gives us a controlled before-and-after comparison of the same user base across model versions.

Since GPT-5.4 and 5.5 use the same tokenizer family, we don't need to control for tokenizer differences. The comparison is direct: same users, same workflows, different model version.

GPT-5.5 Is Less Verbose, But Only for Longer Prompts

Using OpenRouter's consistent token counts, we measured how completion lengths changed between models:

Prompt Size Median Completion (5.4) Median Completion (5.5) Change
< 2K tokens 121 129 +7%
2K – 10K 140 213 +52%
10K – 25K 211 143 -32%
25K – 50K 185 150 -19%
50K – 128K 188 136 -28%
128K+ 215 143 -34%

For prompts above 10K tokens, GPT-5.5 produces 19-34% fewer tokens. For shorter prompts, the pattern reverses: under 2K tokens completions are roughly the same length, and in the 2K-10K range they are 52% longer.

Actual Cost Impact

Using billed costs from requests in the switcher cohort, we calculated the average cost per million OpenRouter tokens. This normalizes for prompt length, allowing a direct comparison of cost efficiency.

Prompt Size Avg $/M OR Tokens (5.4) Avg $/M OR Tokens (5.5) Change
< 2K tokens $4.89 $9.37 +92%
2K – 10K $2.25 $3.81 +69%
10K – 25K $1.42 $2.15 +51%
25K – 50K $1.02 $1.65 +62%
50K – 128K $0.74 $1.10 +49%
128K+ $0.71 $1.31 +85%

Our analysis shows that GPT-5.5 actual costs increased 49% to 92%. Longer prompts, over 10k tokens, saw costs offset by shorter completions. Shorter prompts, under 10k, experience a higher cost increase where completions did not get shorter.

Methodology

  • Source: OpenRouter's request logs
  • Cohort: Users whose top model by request count was GPT-5.4, who then switched to GPT-5.5 as their top model
  • Sample size: Text-only, non-cancelled requests split across 5.4 and 5.5
  • Windows: GPT-5.4: April 21-23, 2026 (pre-launch); GPT-5.5: April 25-28, 2026 (post-launch, launch day excluded)
  • Normalization: Cost per million OpenRouter tokens, bucketed by prompt token count. OpenRouter counts tokens independently from OpenAI, providing a consistent baseline across model versions.
  • Controls: Excluded media (images, files, audio, video), cancelled requests, and zero-token requests
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