Every figure quoted in this video, where it came from, and the arithmetic behind it. All prices are US dollars per million tokens unless stated otherwise. All benchmark scores and prices are as published in late July 2026; both move often, so treat the dates as part of the figure.
| What | Where |
|---|---|
| SWE-bench Verified leaderboard, 29 July 2026 | https://benchlm.ai/benchmarks/sweVerified |
| SWE-bench Verified, open weights flags and vendor prices | https://llm-stats.com/benchmarks/swe-bench-verified |
| SWE-bench Pro leaderboard | https://llm-stats.com/benchmarks/swe-bench-pro |
| DeepSeek API pricing, verified 25 July 2026 | https://deepseek.ai/pricing |
| DeepSeek V4 Pro specs and pricing | https://openrouter.ai/deepseek/deepseek-v4-pro |
| DeepSeek V4 licence, parameters, weight size, hardware | https://localaimaster.com/models/deepseek-v4 |
| Anthropic API pricing, batch and cache rates | https://benchlm.ai/anthropic/api-pricing |
| Cost per task method and comparison table | https://ssojet.com/blog/cheapest-ai-coding-models |
| Measured cost per task on a real harness | https://github.com/Vexp-ai/vexp-swe-bench |
| Token usage per task type, cache read share | https://tokenade.net/en/stats/token-usage-by-task-type |
| H200 cloud pricing across 33 providers | https://getdeploying.com/gpus/nvidia-h200 |
| Kimi K3 pricing and weights status | https://www.digitalapplied.com/blog/open-weight-model-wave-july-2026-momentum-tracker |
SWE-bench Verified is 500 human validated GitHub issues taken from real repositories. A model is scored on whether the patch it produces makes the repository’s own tests pass, so the score is a resolved rate rather than a similarity judgement.
Scores on SWE-bench Verified, 29 July 2026:
| Model | Score | Weights |
|---|---|---|
| Claude Opus 5 | 96.0 | closed |
| Claude Mythos 5 | 95.5 | closed |
| Claude Fable 5 | 95.0 | closed |
| Ornith 1.0 397B | 82.4 | open |
| Claude Opus 4.6 | 80.8 | closed |
| DeepSeek V4 Pro (Max) | 80.6 | open |
| Gemini 3.1 Pro | 80.6 | closed |
| MiniMax M3 | 80.5 | open |
| Kimi K2.6 | 80.2 | open |
| GPT-5.2 | 80.0 | closed |
| DeepSeek V4 Flash (Max) | 79.0 | open |
Two gaps are quoted from that table:
96.0 - 82.4 = 13.6 points.80.8 - 80.6 = 0.2 points. Opus 4.6 is a
previous generation flagship, which is the basis for the claim that open weights
caught the prior frontier rather than the current one.Gemini 3.1 Pro at 80.6 is level with DeepSeek V4 Pro to the tenth of a point, and GPT-5.2 sits at 80.0, which is why the video says closed models are also clustered at that level rather than the cluster being an open weights problem.
DeepSeek V4 Pro Max and DeepSeek V4 Flash Max are the highest reasoning effort settings of those two models. Lower settings score lower on the same board: V4 Pro (High) 79.4, V4 Flash (High) 78.6.
SWE-bench Pro is the harder variant, built from longer multi step tasks that need extended reasoning rather than a single localised fix. Scores:
| Model | Score |
|---|---|
| Claude Fable 5 | 80.0 |
| GLM 5.2 | 62.1 (top open weights) |
| MiniMax M3 | 59.0 |
| Kimi K2.6 | 58.6 |
| DeepSeek V4 Pro Max | 55.4 |
80.0 - 55.4 = 24.6 points. That is the figure behind the claim that the gap widens
as tasks get closer to real work: 13.6 points on Verified becomes 24.6 points on Pro
for the same pair of vendors.
DeepSeek V4 shipped on 24 April 2026 with weights and code under a single MIT licence, which permits commercial use, fine tuning and redistribution.
Kimi K3 is quoted as a counterexample to open meaning cheap: $3.00 input and $15.00 output, with open weights announced for late July 2026 and not published as of the figures above.
| Model | Input | Cache read | Output |
|---|---|---|---|
| DeepSeek V4 Pro | $0.435 | $0.003625 | $0.87 |
| DeepSeek V4 Flash | $0.14 | $0.0028 | $0.28 |
| Kimi K3 | $3.00 | $0.30 | $15.00 |
| Claude Opus 5 | $5.00 | $0.50 | $25.00 |
Anthropic cache reads are 10 per cent of the base input rate, so $5.00 x 0.10 = $0.50.
Anthropic’s batch API takes 50 per cent off both directions, giving Claude Opus 5
batch rates of $2.50 input and $12.50 output, with results returned within 24 hours.
Per token multiples between DeepSeek V4 Pro and Claude Opus 5:
5.00 / 0.435 = 11.49, quoted as 11.5 times.25.00 / 0.87 = 28.74, quoted as 28.7 times.0.50 / 0.003625 = 137.9, quoted as 138 times.The comparison uses one fixed token budget per attempt for every model, so the only things that vary are the published prices and the published solve rate. The budget is 50,000 input tokens for repository context and conversation history, and 12,000 output tokens for reasoning and the patch. Cost per resolved task is the cost of one attempt divided by the solve rate, which prices in the fact that a failed attempt still costs a full attempt.
DeepSeek V4 Pro, one attempt:
input 0.050M x $0.435 = $0.021750
output 0.012M x $0.870 = $0.010440
attempt = $0.032190
per resolved task = 0.032190 / 0.806 = $0.039938 -> $0.040
Claude Opus 5, one attempt:
input 0.050M x $5.00 = $0.250000
output 0.012M x $25.00 = $0.300000
attempt = $0.550000
per resolved task = 0.55 / 0.96 = $0.572917 -> $0.573
0.572917 / 0.039938 = 14.34, quoted as 14.3 times. The solve rate advantage of the
more expensive model is already inside that number, because it is the divisor.
Claude Opus 5 on the batch API:
input 0.050M x $2.50 = $0.125
output 0.012M x $12.50 = $0.150
attempt = $0.275
per resolved task = 0.275 / 0.96 = $0.286458 -> $0.286
0.286458 / 0.039938 = 7.17, quoted as about 7 times the open model’s standard price.
As a sanity check on the modelled figure, an open harness measured $0.67 per task running Claude Code over a 100 task subset of SWE-bench Verified at a 73 per cent pass rate. That is the same order as the $0.573 the fixed budget model predicts.
Over 100 tickets, using the same attempt cost and the published solve rates:
Claude Opus 5 100 x $0.550000 = $55.00 resolves 96.0
DeepSeek V4 Pro 100 x $0.032190 = $ 3.22 resolves 80.6
saving = $51.78
extra unresolved 96.0 - 80.6 = 15.4 tickets
saving per extra failure 51.78 / 15.4 = $3.3624 -> $3.36
At a chosen rate of $100 per hour for engineer time, $100/60 = $1.6667 per minute, so
3.3624 / 1.6667 = 2.02 minutes. That is the two minute figure: each extra failure
has to be diagnosed and fixed in about two minutes before the saving is gone. The
$100 per hour rate is a stated assumption, not a measured figure; the conclusion
scales linearly with whatever rate you substitute.
The 15.4 extra failures are not a random sample of the backlog. The SWE-bench Pro figures above show the same vendors separating by 24.6 points rather than 13.6 once tasks get longer and need multi step reasoning, so the failures concentrate in the harder tail.
A coding task in the SWE-bench class consumes between 1 million and 3.5 million tokens per task in measured agentic runs, including retries and self correction. The same task run twice can differ by up to 30 times in total tokens, and higher token consumption does not correlate with higher accuracy.
In a production agent workflow, 82 to 83 per cent of input tokens were cache reads: context the agent has already paid to read, being re read as the conversation grows.
The 62,000 token attempt used in the fixed model is therefore optimistic by between 16 and 56 times against that measured range.
Modelling one task as 1.5 million input tokens with 83 per cent of them served as cache reads, plus 100,000 output tokens:
cache reads 1.5M x 0.83 = 1.245M
new input 1.5M x 0.17 = 0.255M
output = 0.100M
total = 1.600M
Those are the token volumes shown on screen: cache reads 1.25M, new input 0.26M,
output 0.10M. As a share of every token processed, cache reads are
1.245 / 1.600 = 77.8 per cent, which is the sense in which cache reads are the
largest single line on the bill.
DeepSeek V4 Pro:
1.245M x $0.003625 = $0.004513
0.255M x $0.435 = $0.110925
0.100M x $0.870 = $0.087000
total = $0.202438
per resolved task = 0.202438 / 0.806 = $0.251164 -> $0.25
Claude Opus 5:
1.245M x $0.50 = $0.622500
0.255M x $5.00 = $1.275000
0.100M x $25.00 = $2.500000
total = $4.397500
per resolved task = 4.3975 / 0.96 = $4.580729 -> $4.58
4.580729 / 0.251164 = 18.24, quoted as 18.2 times. The multiple grows from 14.3 to
18.2 once cache reads are priced in, because DeepSeek’s cache read discount is far
deeper than Anthropic’s: 99.2 per cent off its own input rate against 90 per cent off.
Redoing the 100 ticket comparison at these volumes:
DeepSeek V4 Pro 100 x $0.202438 = $ 20.24 resolves 80.6
Claude Opus 5 100 x $4.397500 = $439.75 resolves 96.0
saving = $419.51
saving per extra failure 419.51 / 15.4 = $27.24
At $100 per hour, 27.24 / 1.6667 = 16.3 minutes, quoted as 16 minutes. The headroom
per failure grows from two minutes to sixteen purely because the task got bigger, which
is the reversal the video describes.
DeepSeek V4 Pro’s BF16 weights are about 3.2 TB on disk. Quantised to four bits they are about 800 GB.
An eight GPU H200 node provides 8 x 141 GB = 1,128 GB of VRAM, so the full precision
weights do not fit on a single node while the four bit version does.
H200 cloud pricing varies widely by provider. On demand rates run from about $2.30 to $5.99 per GPU hour, with a tracked median around $4.22. An eight GPU H200 node is quoted at $34.32 per hour.
$34.32 x 24 x 30 = $24,710.40 per month, running continuously
Against DeepSeek’s own API price for a task at the real volumes above:
24,710.40 / 0.202438 = 122,065 tasks per month -> about 122,000
122,065 / 30 = 4,069 tasks per day -> about 4,000
Below roughly 4,000 tasks a day, paying DeepSeek’s API is cheaper than renting the hardware to serve the same model, before any staff time to operate it.