If I'm reading the benchmarks right, they now went from being much cheaper than Luna (but twice as slow), to being roughly same price (but twice as slow).
So all else being equal, where I would previously have used DeepSeek, I can just use Luna, and get the same result twice as fast?
(Yeah I know benchmarks are mostly nonsense, but the ones measuring time are real, and it's the most precious resource.)
(Input / Output / Cache Read, [$/M])
DeepSeek-V4-Flash:
Prev: 0.14 / 0.28 / 0.0028
Off-Peak: 0.22 (1.6x) / 0.66 (2.4x) / 0.007 (2.5x)
Peak: 0.44 (3.1x) / 1.32 (4.7x) / 0.014 (5.0x)
DeepSeek-V4-Pro:
Prev: 0.435 / 0.87 / 0.003625
Off-Peak: 0.66 (1.5x) / 1.98 (2.3x) / 0.022 (6.1x)
Peak: 1.32 (3.0x) / 3.96 (4.6x) / 0.044 (12.1x)
EDIT: formatting
EDIT2: giving up on the formatting :-/
Keep at it, I believe in you.
Provider, Model Billing Input Output Cache read Cache write
DeepSeek
V4-Flash Old $0.1400 $0.2800 $0.0028 -
V4-Flash New Off-Peak $0.2200 (1.6x) $0.6600 (2.4x) $0.0070 (2.5x) -
V4-Flash New Peak $0.4400 (3.1x) $1.3200 (4.7x) $0.0140 (5.0x) -
V4-Pro Old $0.4350 $0.8700 $0.0036 -
V4-Pro New Off-Peak $0.6600 (1.5x) $1.9800 (2.3x) $0.0220 (6.1x) -
V4-Pro New Peak $1.3200 (3.0x) $3.9600 (4.6x) $0.0440 (12.1x) -
OpenAI
GPT-5.6 Sol $5.0000 $30.000 $0.5000 $6.2500
GPT-5.6 Terra $2.0000 $12.000 $0.2000 $2.5000
GPT-5.6 Luna $0.2000 $1.2000 $0.0200 $0.2500
Anthropic
Claude Fable 5 $10.000 $50.000 $1.0000 $12.500
Claude Opus 5 $5.0000 $25.000 $0.5000 $6.2500
Claude Sonnet 5 $2.0000 $10.000 $0.2000 $2.5000
Moonshot
Kimi K3 $3.0000 $15.000 $0.3000 -
Z.AI
GLM 5.2 $1.4000 $4.4000 $0.2600 -p.s. Thanks DSv4-Flash, for your hard work of converting a messy table into plain text.
DeepSeek was hugely underpricing cache hit pricing before and even after this increase they're still cheaper on that metric than every other provider I'm aware of, but it will put an end to those "I used 1 billion tokens and spent $4" reports.
I think that Flash is still a usable model but Pro is DOA... Even before the price difference between Flash and Pro, vs the intelligence / problem solving / tool calling did not make sense. But now that gap has widen even more. And there are just too many competitors models now close to that Pro price range.
Especially when we compare that competitive models offer subscription services that easily cut down the token price by 1:10. That makes Pro especially a bad value.
We shall see what the 3th party market is going to do, but i suspect that prices will be increased. If the argument was that DeepSeek increases price as they lack capacity, a company with access to billions, other 3th party providers that need to rent and have less optimized infrastructures will increase prices. Especially if they get hit hard with people moving around.
Its like we always see the same issue with popular models.
* GLM 5.2 is good, capacity issues, API price up, subscription heavy nerfs. * Kimi K3 is good, capacity issues, API price up, subscription heavy nerfs. * DeepSeek V4 GA is good, capacity issues, API price up * OpenAI GLM 5m, 10m active users. Subscription usage is sneakily tightened more and more. * Anthropic Opus too popular, ...
That is the main issue. The AI users are people who actively easily move between companies. Pushing peak loads to each unprepared company, releasing load on the "less desired". And round we go ...
I pasted the same prompt into OpenCode, set to Deepseek v4 flash free and did it first try.
I'm was going to purchase Opencode GO to try it, but seems my timing is really bad :( hope it doesn't go up too much in Opencode or they find other providers. Bad timing!
Right now, they give 4100 credits for Luna and 63 000 for Deepseek on their prepaid plan (both are 2x)
Do they just set a super low caching time and hope that drops effective cache rates low enough? Do all other providers somehow overcharge by that much? Are they just going to sell it as a loss leader?
This, I think. Cached inputs have an opportunity cost (keeping the KV cache until use) but a hit is basically free. “Basically” - if the cache is offloaded to system RAM or NVMe there’s some scheduling overhead.
From a consumer viewpoint a more interesting metric than the raw costs is
cached cost * hitrate + input cost * (1 - hitrate)
This can somewhat be the case, depending on your config. I updated mine to make DeepSeek high priority because I was having a lot of cache misses and reliability issues with the default (cheapest (at face value)) providers, and cost was actually higher overall than anticipated. Was smooth sailing from then; might have to tweak things again now pricing has changed though.
I don't have a clue on what the real cost to inference providers comes out to, but it seems really weird that there would be such a big gap, in what should be a pretty competitive market.
Thank you in advance!
But DeepSeek v4 Pro is a far more capable model and still cheaper than anything that it competes with, from what I can see.
If you check on OpenRouter, some other providers serve V4 Flash at seemingly cheaper normal input/output tokens rates, but with a huge caveat: they have at least a 5x increase of the cache hit cost of the official API, some have a 10x+. No provider comes close to Deepseek's old low cache prices, and cache is 90%+ of what matters in agentic sessions.
Closest comparison:
- Deepseek: $0.14/$0.28 with $0.0028 cache hit cost for official API
- DeepInfra: $0.08/$0.18 (cheaper base rates!) with $0.016 cache hit (almost 6x!! Deepseek's current cache cost)
Another great example is Kimi K3, official API is $3/$15 and the cheapest provider on OpenRouter is $2.8/$14, only a tiny difference.
[1] https://nitter.net/thdxr/status/2085377844515922210#m
[2] https://nitter.net/thdxr/status/2087610161636471289#m
Let's say DeepSeek is being forced to use the CANN stack, and the new pricing reflects the cost when 100% of inference is done with Huawei chips. Then, I suppose we can infer that:
* CANN stack is 1.5x~2.3x less efficient in compute
* CANN stack has 6x lower inter-connect capacity
> computer chips once again become a commodity
Ascend 950 is going for $7k to $9k with mediocre looking specs. $16k for RTX Pro 6000, $6k for RTX Pro 5000. This is not looking good.
The CEO of DeepSeek recently revealed to investors a lot about the resources available to DeepSeek, and the gap between Huawei and NVIDIA. Select quotes from the transcript (translation is a bit patchy on the source website though):
"We currently have roughly 20,000 H-equivalent compute cards"
"Huawei 950—right now Huawei gives us 16,000 cards, this should be publicly stateable."
"Like Huawei gives us roughly 16,000 cards of capacity, internet giants maybe get a hundred-something thousand, we get ten-something thousand—I think this ratio is also relatively... but this is probably just how much capacity Huawei has."
"16,000 Huawei 950 cards only equal 4,000 B-series cards."
"Huawei’s supernode, Huawei’s 950 supernode, in performance and price can completely substitute for NVIDIA’s GB200, GB300. The price is definitely more expensive, but limitedly so. Fifty percent more expensive, a hundred percent more expensive—a hundred percent more doesn’t matter, two hundred percent more doesn’t matter. For example, a hundred percent more expensive—I think it can already be considered a price-level substitute."
"I think domestic hardware might need a few years."
"I don’t quite believe that five years from now, we’ll still be stuck on the production capacity problem. Right now we’re definitely stuck on the production capacity problem—this year, next year, the year after, I think we might still be stuck on the production capacity problem, but five years later, I think maybe not necessarily—I’m still relatively optimistic."
[1] https://www.fredgao.com/p/deepseeks-liang-wenfeng-breaks-his