TokenMix Research Lab · 2026-05-08
GPT-5.5 vs Opus 4.7 vs DeepSeek V4 (2026): 50x Price Gap Tested
DeepSeek V4 Flash output is 107x cheaper than GPT-5.5 and 89x cheaper than Claude Opus 4.7, but Opus 4.7 still leads SWE-Bench Pro at 64.3% and GPT-5.5 wins Intelligence Index at 60. Pick by workload, not by sticker price.
In a six-week window from April 16 to April 24 of 2026, three frontier-class models shipped: Anthropic's Claude Opus 4.7 on April 16, OpenAI's GPT-5.5 on April 23, and DeepSeek V4 Preview on April 24. Based on Anthropic's official Opus pricing, Opus 4.7 lists at $5 input / $25 output per 1M tokens with a 1M-token context window at standard rates. OpenAI's API pricing page puts GPT-5.5 at $5 / $30 — a 2x increase over GPT-5.4's $2.50 / $15 — with prompts above 272K tokens charged at 2x input and 1.5x output. DeepSeek's pricing docs list V4 Flash at $0.14 / $0.28 (cache hit and miss identical), and V4 Pro at a promotional $0.435 / $0.87 through 2026-05-31 15:59 UTC, after which V4 Pro reverts to $1.74 / $3.48. On benchmarks, Vellum's testing confirms Opus 4.7 at 87.6% SWE-Bench Verified and 64.3% SWE-Bench Pro (up from 53.4% on Opus 4.6), Artificial Analysis ranks GPT-5.5 (xhigh) #1 at Intelligence Index 60 with Opus 4.7 at 57 and DeepSeek V4 Pro at 52, and DeepSeek V4 Pro Max scores 80.6% SWE-Bench Verified per BenchLM's three-way comparison. The gap between cheapest output ($0.28 on V4 Flash) and most expensive ($30 on GPT-5.5) is 107x — but the right answer depends on whether you're optimizing latency, agentic coding quality, or steady-state throughput.
Table of Contents
- Quick Answer
- Confirmed Facts (May 2026)
- How Does Pricing Stack Up Across the Three Frontier Models?
- Which Model Wins on SWE-Bench Pro, Verified, and Terminal-Bench?
- When Does the 35x Price Gap Actually Matter?
- Real Cost Examples: Four Production Workloads Compared
- Should You Pick One Model or Route Across All Three?
- Why Does Opus 4.7's New Tokenizer Add 5-35% to Your Bill?
- API Fields To Log When Routing Between These Three
- Final Recommendation
- FAQ
- Related Articles
- Sources
Quick Answer
| Question | One-Line Answer |
|---|---|
| Which is cheapest? | DeepSeek V4 Flash at $0.14 input / $0.28 output per 1M tokens |
| Which wins agentic coding? | Claude Opus 4.7 — leads SWE-Bench Pro (64.3%) and MCP-Atlas (77.3%) |
| Which wins general intelligence? | GPT-5.5 (xhigh) — Intelligence Index 60, Terminal-Bench 2.0 82.7% |
| Which has 1M context cheapest? | DeepSeek V4 Pro / Flash — both 1M tokens, no surcharge; GPT-5.5 charges 2x above 272K |
| What's the dirty secret? | Opus 4.7's new tokenizer makes the same prompt cost 5-35% more than Opus 4.6 |
Confirmed Facts (May 2026)
The three frontier models behave very differently — confirmed pricing and benchmarks below, with caveats and inferences clearly separated so you can quote what's verified and discount what's not.
| Status | Fact | Primary Source |
|---|---|---|
| Confirmed | GPT-5.5 standard pricing $5 / $30 per 1M tokens; 2x of GPT-5.4 | OpenAI API pricing |
| Confirmed | Claude Opus 4.7 pricing $5 / $25 per 1M tokens; cache 90% off, batch 50% off | Anthropic Opus |
| Confirmed | DeepSeek V4 Flash $0.14 / $0.28 (no cache discount); V4 Pro $0.435 / $0.87 promo until 2026-05-31 15:59 UTC | DeepSeek pricing |
| Confirmed | Opus 4.7 SWE-Bench Pro 64.3% — up 10.9pp from Opus 4.6's 53.4% | Anthropic launch post + Vellum |
| Confirmed | GPT-5.5 leads Terminal-Bench 2.0 at 82.7% for agentic terminal workflows | Artificial Analysis leaderboard |
| Confirmed | DeepSeek V4 architecture: V4 Pro 1.6T params (49B activated), V4 Flash 284B (13B activated), both MoE with 1M context | Hugging Face DeepSeek-V4-Pro |
| Caveat | Opus 4.7's new tokenizer encodes the same English text into 5-35% more tokens than Opus 4.6, raising real bills despite identical sticker | Finout breakdown + CloudZero |
| Caveat | DeepSeek V4 Pro reverts to $1.74 / $3.48 on June 1, 2026 — write contracts assuming post-promo pricing | DeepSeek pricing |
| Inferred | Even at post-promo rates, V4 Pro stays ~9x cheaper than GPT-5.5 output and ~7x cheaper than Opus 4.7 output | Calculation from listed prices |
| Inferred | GPT-5.5 long-context surcharge above 272K tokens makes 1M-token requests effectively double-priced versus headline rate | OpenAI pricing schedule |
How Does Pricing Stack Up Across the Three Frontier Models?
Output pricing spans a 107x range — DeepSeek V4 Flash at $0.28 versus GPT-5.5 at $30 per 1M tokens — and that gap drives every routing decision below.
| Model | Input ($/1M) | Output ($/1M) | Cache Hit | Batch (50% off) | Long Context |
|---|---|---|---|---|---|
| GPT-5.5 | $5.00 | $30.00 | not offered | $2.50 / $15.00 | >272K: 2x in, 1.5x out |
| GPT-5.5 Pro | $30.00 | $180.00 | not offered | varies | priority $75 / $450 |
| Claude Opus 4.7 | $5.00 | $25.00 | $0.50 input (90% off) | $2.50 / $12.50 | 1M std, no surcharge |
| DeepSeek V4 Flash | $0.14 | $0.28 | $0.14 (no discount) | not offered | 1M std, no surcharge |
| DeepSeek V4 Pro (promo until 5/31) | $0.435 | $0.87 | $0.003625 (99% off) | not offered | 1M std, no surcharge |
| DeepSeek V4 Pro (post-promo, 6/1+) | $1.74 | $3.48 | $0.0145 | not offered | 1M std, no surcharge |
Based on OpenAI's API pricing, GPT-5.5's $5 / $30 represents a 2x increase over GPT-5.4's $2.50 / $15. Anthropic prices Opus 4.7 at $5 / $25, making it 17% cheaper on output than GPT-5.5 but 89x more expensive on output than DeepSeek V4 Flash at $0.28. Pricing may vary by region, contract, or volume tier, as noted in DeepSeek's documentation; numbers above reflect publicly listed prices verified on 2026-05-09. For deeper breakdowns, see TokenMix's Claude API Pricing 2026 and DeepSeek Cache Hit Pricing pages — both updated weekly against vendor docs.
A second-order observation: GPT-5.5's batch pricing of $2.50 / $15 is identical to GPT-5.4's standard pricing, confirming OpenAI's pattern of using batch discounts to keep async workloads at last-generation prices. Anthropic's prompt caching is the steepest single discount in the table — 90% off cached input — but only applies to repeated prefixes within a 5-minute window, so it's most valuable for chat applications, not one-shot agent calls.
Which Model Wins on SWE-Bench Pro, Verified, and Terminal-Bench?
Opus 4.7 leads coding benchmarks (SWE-Bench Pro 64.3%, MCP-Atlas 77.3%), GPT-5.5 leads general reasoning (Intelligence Index 60, Terminal-Bench 2.0 82.7%), and DeepSeek V4 Pro lands closer to Opus on coding at roughly 1/35th the price.
| Benchmark | GPT-5.5 (xhigh) | Opus 4.7 | DeepSeek V4 Pro Max | Source |
|---|---|---|---|---|
| Intelligence Index | 60 | 57 | 52 | Artificial Analysis |
| SWE-Bench Verified | 88.7% | 87.6% | 80.6% | marc0.dev leaderboard + Vellum |
| SWE-Bench Pro | reported ~58% | 64.3% | reported ~58% | Anthropic launch |
| Terminal-Bench 2.0 | 82.7% | reported ~78% | not yet listed | OpenAI release |
| MCP-Atlas (tool use) | 68.1% | 77.3% | not yet listed | Anthropic blog |
| GPQA Diamond | 92.8% | 91.3% | not yet on AA | Artificial Analysis |
| Output speed (tok/s) | fastest first-token, short tasks | adaptive (varies) | always reasoning pass first | AA serving infra |
Vellum's independent SWE-Bench tests put Opus 4.7's single-release jump at 10.9 percentage points on SWE-Bench Pro — the largest single-version SWE-Pro gain Anthropic has shipped to date. Compared to GPT-5.5's Terminal-Bench 2.0 lead at 82.7%, Opus 4.7 dominates a different agentic axis: tool use and code-edit loops. DeepSeek V4 Pro Max trails by 7-9pp on coding benchmarks but does so at output prices that are 34x lower during the promo window — a tradeoff most cost-sensitive teams will accept on non-critical loops.
When Does the 35x Price Gap Actually Matter?
The 35x output gap between DeepSeek V4 Flash and GPT-5.5 only matters above ~10M output tokens/month — below that threshold, latency, integration cost, and answer quality dominate the bill, not the per-token rate.
| Monthly Output Tokens | GPT-5.5 Cost | Opus 4.7 Cost | V4 Flash Cost | Savings (V4 Flash vs GPT-5.5) |
|---|---|---|---|---|
| 100K | $3 | $2.50 | $0.028 | $2.97 |
| 1M | $30 | $25 | $0.28 | $29.72 |
| 10M | $300 | $250 | $2.80 | $297.20 |
| 100M | $3,000 | $2,500 | $28 | $2,972 |
| 1B | $30,000 | $25,000 | $280 | $29,720 |
At 1M output tokens/month a team saves $30 by routing to V4 Flash instead of GPT-5.5 — typically less than the engineering cost of writing one prompt-format adapter. At 10M tokens/month savings hit ~$300, roughly the break-even point where dedicated routing infrastructure starts paying for itself. Above 100M, the $3,000+ monthly delta justifies any reasonable orchestration cost. Based on OpenAI's standard pricing and DeepSeek's V4 Flash rate, this break-even math holds even before factoring in batch and cache discounts — both of which favor the cheap providers (DeepSeek V4 Pro cache hit drops to $0.003625, an effective 99% discount). On TokenMix.ai's pricing tracker you can run this calculation against live rates for 300+ models, including the GPT-5.5 long-context surcharge that doubles real cost on >272K-token requests.
Real Cost Examples: Four Production Workloads Compared
Across four common production workloads — coding agent, support chatbot, RAG pipeline, long-context summarization — DeepSeek V4 Flash wins on raw cost by 25-100x, but Opus 4.7 wins by 2-3x on per-task ROI when output quality drives downstream value.
1. Coding Agent: 1M Output Tokens/Day
Assume 1M output tokens/day with 4M input tokens/day (typical agent loop with code reading + edits). Monthly tokens: 30M out, 120M in.
| Model | Input Cost | Output Cost | Total/Month | vs GPT-5.5 |
|---|---|---|---|---|
| GPT-5.5 | $600 | $900 | $1,500 | baseline |
| Opus 4.7 | $600 | $750 | $1,350 | -10% |
| Opus 4.7 (with 50% cache hit) | $360 | $750 | $1,110 | -26% |
| DeepSeek V4 Pro (promo) | $52.20 | $26.10 | $78.30 | -94.8% |
| DeepSeek V4 Pro (post-promo) | $208.80 | $104.40 | $313.20 | -79.1% |
| DeepSeek V4 Flash | $16.80 | $8.40 | $25.20 | -98.3% |
V4 Flash is cheapest, but per BenchLM's three-way coding shootout, it lands ~12pp below Opus 4.7 on multi-step tool-use benchmarks — meaning the agent retries more, eating part of the savings. For coding agents where each run produces customer-facing PRs, Opus 4.7 with prompt caching is the rational pick. For internal scripts, V4 Pro promo wins.
2. Customer Support Chatbot: 5K Conversations/Day, ~500 Output Tokens Each
Monthly: 75M output tokens, ~225M input (3:1 input:output for chat with system prompt + history).
| Model | Cost/Month | Caveat |
|---|---|---|
| GPT-5.5 | $3,375 | high quality but premium-priced |
| Opus 4.7 (with cache) | $1,800 | cache amortizes system prompt |
| Opus 4.7 (no cache) | $3,000 | only worth picking with batch processing |
| DeepSeek V4 Flash | $52.50 | cheapest, latency acceptable for chat |
V4 Flash dominates on cost. Latency is acceptable for chat (sub-second first-token from Artificial Analysis serving data). Quality gap matters less when most queries are routine FAQ.
3. RAG Pipeline: 10K Queries/Day, ~200 Output Tokens
Monthly: 60M output tokens, ~600M input (RAG = long context). Use V4 Flash or Opus 4.7 to avoid GPT-5.5's 272K surcharge if individual contexts exceed that threshold.
| Model | Cost/Month | Notes |
|---|---|---|
| GPT-5.5 (under 272K) | $4,800 | premium |
| GPT-5.5 (above 272K, doubled rate) | $9,600 | long-context surcharge stings |
| Opus 4.7 (no cache) | $4,500 | similar to GPT-5.5 |
| Opus 4.7 (90% input cache hit) | $1,800 | best mid-tier choice for stable corpora |
| DeepSeek V4 Flash | $100.80 | wins by 18-95x depending on context size |
4. Long-Context Summarization: 1M-Token Inputs
For 1M-token document summarization at 1K queries/day, 10K output tokens each:
| Model | Cost/Day | Notes |
|---|---|---|
| GPT-5.5 (1M input @ 2x rate) | $10,000 input + $300 output = $10,300 | surcharge effectively doubles cost |
| Opus 4.7 (1M std rate) | $5,000 input + $250 output = $5,250 | no surcharge above 200K |
| DeepSeek V4 Pro (promo) | $435 input + $8.70 output = $443.70 | cheapest viable |
| DeepSeek V4 Flash | $140 input + $2.80 output = $142.80 | cheapest by 35-72x |
Anthropic and DeepSeek's flat 1M-context pricing is the structural advantage here. As Vellum and BenchLM both note in their three-way comparisons, GPT-5.5 is the wrong pick for any workload that lives above the 272K threshold — the surcharge erases the model's quality lead on cost.
Should You Pick One Model or Route Across All Three?
Most production teams should route, not pick: send classification and short Q&A to V4 Flash, send agentic coding to Opus 4.7, send hard reasoning chains to GPT-5.5 (xhigh), and avoid GPT-5.5 entirely for >272K-token requests.
| Task Type | First Choice | Fallback | Why |
|---|---|---|---|
| Short Q&A / classification | DeepSeek V4 Flash | GPT-5.5 batch | 100x cheaper, sub-second latency on AA infra |
| Agentic coding (multi-step) | Claude Opus 4.7 | DeepSeek V4 Pro | Opus leads SWE-Bench Pro and MCP-Atlas; V4 Pro is 34x cheaper fallback |
| Complex reasoning / proofs | GPT-5.5 (xhigh) | Opus 4.7 (Adaptive) | GPT-5.5 leads Intelligence Index 60 and Terminal-Bench 82.7% |
| Long-context summarization (>272K) | DeepSeek V4 Pro / Opus 4.7 | (avoid GPT-5.5) | GPT-5.5 long-context surcharge doubles cost |
| Document Q&A with grounding | Opus 4.7 (Adaptive) | GPT-5.5 short prompts | First-token latency advantage; structured output reliable |
| High-volume embedding pre-processing | DeepSeek V4 Flash | Gemini Flash | sub-cent classification at frontier-grade quality |
The break-even for routing engineering work is roughly $300/month in spend — below that, single-provider simplicity wins. Above that, the savings outpace adapter-maintenance cost. Through TokenMix.ai's unified API, you can route across GPT-5.5, Opus 4.7, V4 Flash, and 297 other models behind one endpoint with shared usage logging — which removes the bookkeeping burden that usually makes routing impractical for small teams. For a head-to-head between the major routers, see TokenMix vs OpenRouter vs Portkey vs LiteLLM.
Why Does Opus 4.7's New Tokenizer Add 5-35% to Your Bill?
Anthropic shipped a new tokenizer with Opus 4.7 that splits the same English text into 5-35% more tokens than Opus 4.6, so identical prompts now cost 5-35% more even though the per-token sticker price is unchanged at $5 / $25.
According to Finout's pricing analysis, the same prompt that consumed N tokens on Opus 4.6 now consumes 1.05N to 1.35N tokens on Opus 4.7 — with the high end concentrated on code-heavy and non-English prompts. CloudZero's deep dive corroborates: real bills rose 5-15% on typical English chat workloads and up to 35% on prompts dominated by code blocks, JSON payloads, or multi-language content. Anthropic discloses this in release notes but does not surface it on the pricing page; teams budgeting against Opus 4.6 baselines will see unexplained spend creep unless they re-tokenize sample prompts before migration.
This is the kind of accounting trap that makes "sticker pricing" comparisons misleading. The fix: re-run your top 10 production prompts through Anthropic's tokenizer endpoint before upgrading, and budget +15% as a safety buffer. TokenMix.ai's per-request usage logs surface the exact usage.input_tokens value from each Anthropic response, so you can detect tokenizer drift directly from billing data rather than discovering it on month-end invoices.
API Fields To Log When Routing Between These Three
Without logging these five fields per provider, you can't reconcile bills when routing — usage object structure differs across all three providers and silent omissions accumulate.
| Field | OpenAI (GPT-5.5) | Anthropic (Opus 4.7) | DeepSeek (V4) |
|---|---|---|---|
| Input tokens | usage.prompt_tokens |
usage.input_tokens |
usage.prompt_tokens |
| Output tokens | usage.completion_tokens |
usage.output_tokens |
usage.completion_tokens |
| Cached input tokens | usage.prompt_tokens_details.cached_tokens |
usage.cache_read_input_tokens |
usage.prompt_cache_hit_tokens |
| Reasoning tokens | usage.completion_tokens_details.reasoning_tokens |
counted in output_tokens |
counted in completion_tokens (V4 Pro Max) |
| Long-context flag | infer from input >272,000 | n/a (no surcharge) | n/a (no surcharge) |
Anthropic exposes cache reads and writes as separate fields (cache_read_input_tokens, cache_creation_input_tokens) — both must be logged or the 90% cache savings disappear from your dashboards. DeepSeek V4 Pro Max output includes reasoning tokens silently; the per-task token count will look 2-5x higher than non-reasoning calls without warning. OpenAI's reasoning_tokens is only populated for reasoning-effort levels (medium/high/xhigh).
Final Recommendation
If you can only pick one: choose Claude Opus 4.7 for agentic coding teams (SWE-Bench Pro 64.3%, MCP-Atlas 77.3%), GPT-5.5 (xhigh) for research-grade reasoning (Intelligence Index 60), or DeepSeek V4 Flash for high-volume classification (107x cheaper than GPT-5.5 output). If you can route, route — the cheapest-to-most-expensive output spread is 107x and no single model wins every workload.
FAQ
Which is cheapest: GPT-5.5, Claude Opus 4.7, or DeepSeek V4?
DeepSeek V4 Flash is cheapest at $0.14 input / $0.28 output per 1M tokens — roughly 36x cheaper than Opus 4.7 input and 107x cheaper than GPT-5.5 output. V4 Pro on the current 75% promo (through 2026-05-31 15:59 UTC) sits at $0.435 / $0.87, then jumps to $1.74 / $3.48 on June 1.
Does Claude Opus 4.7 really cost 35% more after the tokenizer change?
Yes for some prompts. Anthropic shipped a new tokenizer with Opus 4.7 that encodes the same English text into more tokens — 5-15% more on typical English prose, up to 35% more on code-heavy or non-English prompts per Finout and CloudZero analysis. Per-token rates match Opus 4.6, but real bills rise.
Can DeepSeek V4 replace GPT-5.5 for production coding?
Partly. DeepSeek V4 Pro Max scores 80.6% on SWE-Bench Verified versus GPT-5.5's 88.7% — strong but not equivalent. For most agentic coding loops, Opus 4.7 (64.3% on SWE-Bench Pro) leads. V4 Pro is a credible cost-optimized fallback; V4 Flash is not for production coding.
Is the 1M-token context window the same on all three?
Practically yes, but priced very differently. Opus 4.7 includes 1M context at standard pricing. DeepSeek V4 Flash and V4 Pro support 1M tokens with no surcharge. GPT-5.5 charges 2x input and 1.5x output for prompts above 272K — meaning long-context requests effectively cost double sticker rate.
How do batch and cache discounts change the math?
Both halve costs but apply differently. OpenAI Batch API discounts GPT-5.5 to $2.50 / $15 (50% off) for 24-hour async jobs. Anthropic prompt caching cuts Opus 4.7 input to ~$0.50 (90% off) on cache hit within a 5-minute window. DeepSeek V4 Pro cache hit drops input to $0.003625 (99% off during promo). DeepSeek V4 Flash has no cache discount.
Which model has the lowest first-token latency?
GPT-5.5 wins on first-token latency for short, single-shot tasks based on Artificial Analysis serving infrastructure data. Opus 4.7 wins once Adaptive Thinking is enabled. DeepSeek V4 Pro Max always runs a visible reasoning pass first, so first-token latency is in seconds — not subseconds — for any reasoning-on call.
Should I route between three models or pick one?
Route if your monthly spend exceeds ~$300 and your workload is mixed (classification + coding + reasoning). Pick one if your team's volume is below that threshold or your engineers can't maintain prompt-format adapters. The break-even for routing engineering work is roughly $300/month in API spend.
When does DeepSeek V4 Pro stop being 75% off?
On 2026-05-31 at 15:59 UTC, per DeepSeek's pricing page. On June 1, V4 Pro input rises from $0.435 to $1.74 per 1M tokens (4x), and output rises from $0.87 to $3.48 (4x). V4 Flash pricing is unchanged.
Related Articles
- Claude API Pricing 2026: Opus, Sonnet, Haiku Cost Breakdown
- Claude API Cache Pricing: Read vs Write Math
- DeepSeek Cache Hit Pricing: When the 99% Discount Pays Off
- GPT-6 Release Date, Features and Pricing 2026
- TokenMix vs OpenRouter vs Portkey vs LiteLLM
- LLM Leaderboard 2026: SWE-bench, MMLU, Arena Elo
- AI API Gateway in 2026: Why Most Devs Switch
Sources
- OpenAI API pricing — https://openai.com/api/pricing/
- Anthropic Claude Opus pricing page — https://www.anthropic.com/claude/opus
- Anthropic Opus 4.7 launch announcement — https://www.anthropic.com/news/claude-opus-4-7
- DeepSeek API pricing docs — https://api-docs.deepseek.com/quick_start/pricing
- DeepSeek V4 Preview release notes — https://api-docs.deepseek.com/news/news260424
- Hugging Face DeepSeek-V4-Pro model card — https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro
- Hugging Face DeepSeek-V4-Flash model card — https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
- Vellum: Claude Opus 4.7 benchmarks explained — https://www.vellum.ai/blog/claude-opus-4-7-benchmarks-explained
- Artificial Analysis leaderboard — https://artificialanalysis.ai/leaderboards/models
- Artificial Analysis GPT-5.5 vs Opus 4.7 head-to-head — https://artificialanalysis.ai/models/comparisons/gpt-5-5-vs-claude-opus-4-7
- BenchLM: DeepSeek V4 Pro vs Opus 4.7 vs GPT-5.5 — https://benchlm.ai/blog/posts/deepseek-v4-vs-claude-opus-4-7-vs-gpt-5-5
- VentureBeat DeepSeek V4 cost analysis — https://venturebeat.com/technology/deepseek-v4-arrives-with-near-state-of-the-art-intelligence-at-1-6th-the-cost-of-opus-4-7-gpt-5-5
- Finout: Opus 4.7 tokenizer real cost story — https://www.finout.io/blog/claude-opus-4.7-pricing-the-real-cost-story-behind-the-unchanged-price-tag
- CloudZero: Opus 4.7 actual cost analysis — https://www.cloudzero.com/blog/claude-opus-4-7-pricing/
- marc0.dev SWE-Bench leaderboard May 2026 — https://www.marc0.dev/en/leaderboard
- OpenAI GPT-5.5 release post — https://openai.com/index/introducing-gpt-5-5/
Pricing and benchmark data verified against vendor official pages on 2026-05-09. Promotional rates and benchmarks may shift; check vendor docs before locking contracts.
By TokenMix Research Lab · Published 2026-05-09 · Last Updated 2026-05-09 · Data Checked 2026-05-09