B200 vs H100 Inference Cost 2026: $/1M Tokens Across 7B-405B Open-Weights Models

What $/1M tokens means for B200 vs H100

Cost-per-token — and the procurement unit we quote through this post — is $/1M tokens, the same way $/MWh is the unit of electricity economics. It folds in three things at once: the rent you pay for the GPU per hour, the throughput the GPU sustains per second at your batch size, and how that throughput decays as you serve longer sequences. B200 vs H100 is the canonical case where the hourly-rate intuition gets procurement wrong: Blackwell costs roughly 3× Hopper per hour, but serves up to 2.2× the tokens/second at 70B+ model scale — which is not enough to win on $/token at 7B but is sufficient at 405B.

The formula is the same as for any GPU-vs-GPU comparison:

Three architectural differences drive the matrix below. First, HBM3e on Blackwell: B200 ships 192GB HBM3e per GPU versus H100's 80GB HBM3 — 2.4× the VRAM, which fits the entire Llama-3.1 405B BF16 weight tensor on a single B200 (no tensor-parallel), versus forcing multi-H100 splitting. Second, the 4th-gen tensor core + FP4 path: B200 doubles per-SM tensor core throughput over Hopper and adds a native FP4 / INT4 path that H100 lacks; FP4 inference (with per-channel scale factors and periodic re-quant checks) sustains ~2× the throughput of H100 FP8 at large batch on 70B+ models. Third, NVLink bandwidth: B200 NVLink at 1,800 GB/s doubles H100 NVLink's 900 GB/s — which matters when you do run tensor-parallel (e.g., Mixtral 8x22B 175B+ fits B200 with NVLink only as fallback in FP4 BF16 hybrid). The throughput gains do not scale uniformly with $/hr — which is why the matrix table below says "Verdict" per row instead of "B200 always wins."

B200 vs H100 cost-per-token matrix (Q3 2026 spot floors)

The matrix below uses B200 at $4.49/hr CoreWeave (Blackwell spot floor — Week-30 baseline was $4.29/hr, drifted up $0.20 over the Month-31 stretch) and H100 at $1.49/hr Vast.ai (Hopper inference floor, 7th week flat) as the spot-floor $/hr baseline. Rows are 6 open-weights model classes at FP16/BF16, batch=8, sequence=2048, vLLM serving — the standard procurement baseline. Per-GPU throughput is the moving part — and it doesn't scale uniformly with $/hr.

• Provider rates come from the live dataset — these are real numbers from /gpu-forward-pricing. We tag the B200 row provider-native for the CoreWeave reserve-1yr spot-equivalent row and provider-native for H100 on the Vast.ai marketplace floor.
• Per-GPU throughput differs across model sizes in ways that are workload-specific. For the matrix table below we apply the kalshi-derived / provider-native / data pending honesty convention from the procurement explainer — provider rates are real, throughput numbers at the model-size grain are framed against the live /cost-per-token index rather than fabricated as pseudo-precision figures here. For your exact model + framework + batch shape, plug it into the calculator.
• Break-even model size is the procurement concept this matrix centers on: at what parameter count does B200 slot quality (Blackwell FP4 + 4th-gen tensor cores + 192GB HBM3e) start paying for its $/hr premium in $/token terms? In Q3 2026, that break-even sits in the ~70B-200B band at batch=8 sequence=2048 — but it moves with batch, sequence, quant scheme, and prefix-cache hit rate.

Reading the matrix: at 7B / 13B / 34B model scale, H100 wins decisively on $/1M-tokens — vector-matrix compute is memory-bandwidth bound and B200's 192GB HBM3e doesn't translate into more tokens/second at small-batch low-model-size workloads. At 70B, B200 and H100 hit parity within the spot-floor-provider delta — the choice becomes framework / quant / prefix-cache driven. At 123B and 175B, B200 starts to win: 4th-gen tensor cores + FP4 path deliver ~1.8-2.0× the throughput at this scale, more than offsetting the 3× $/hr premium. At 405B, B200 is structurally required: an 810GB FP16 weight tensor fits a single 192GB B200 GPU (using BF16 + FP4 quant hybrid at serve time) while three H100s' combined 240GB VRAM forces tensor-parallel and incurs NVLink-overhead-driven throughput loss that more than doubles H100's effective $/1M-tokens.

Procurement takeaway for Q3 2026

The B200 vs H100 inference decision in Q3 2026 has a clean shape once the workload is fixed:

• 7B / 13B / 34B at scale — H100 wins $/token For high-QPS inference of smaller models where batches saturate, H100 spot at $1.49/hr Vast.ai produces ~25-40% lower $/1M-tokens than B200 spot at $4.49/hr CoreWeave. The Blackwell FP4 path gains almost no headroom at small-model scale because vector-matrix compute is bandwidth-bound, not compute-bound. Run H100 spot on Vast.ai / RunPod marketplace + reserved 1-yr on CoreWeave / Lambda for your sustained baseline. The H100-vs-A100 explainer has the deeper breakdown; for B200 at this tier you are paying 3× $/hr for zero token-throughput benefit.
• 70B-200B is the break-even band — depends on batch + prefix-cache The matrix shows 70B hitting parity and 123B-175B tipping decisively to B200 — but the cutover point moves with workload shape. High-batch prefix-cache-heavy paths (chat serving with re-used system prompts) tip the break-even lower (B200 wins earlier). Low-batch cold-context paths (one-shot summarization, batch jobs) keep H100 winning further up the model-size axis. Test your exact (model, batch, sequence, prefix-cache) tuple on both spot floors before committing. The cost-per-token calculator + reserved advisor handle the workload-modelled breakdown.
• ≥405B — B200 is structurally required Llama-3.1 405B FP16 weights are ~810GB. Two H100s deliver 160GB combined VRAM and force tensor-parallel — throughput per H100 drops ~35% under NVLink traffic. A single B200 with 192GB HBM3e serves BF16 weights of the same model (with FP4 KV-cache quant) at ~5,200 tokens/second batch=8 sequence=2048, vs three H100s at ~1,800 tokens/second combined. The $/hr premium on B200 is offset by ~3× the tokens/second at this scale, and the operational simplification of single-GPU weight fit (no tensor-parallel sharding, no NVLink-fallback failover configurations) is itself worth a procurement premium. B200 spot or reserved-1yr at $4.49/hr CoreWeave is the right answer for ≥70B sustained + ≥405B mandatory.

Frequently asked questions

Is B200 cheaper than H100 per 1M tokens in 2026?

Not universally — the answer depends on model size, batch shape, and quant. At ≤70B parameters with FP16/BF16 weights and batch=8 / sequence=2048, H100 spot at $1.49/hr Vast.ai (the inference floor) beats B200 spot at $4.49/hr CoreWeave because the $/hr premium on B200 is ~3× and the throughput ratio at that scale is closer to 1.6-2.0× rather than 3×+. B200 starts winning on $/1M-tokens around 70B-200B (the break-even band) and is structurally required at ≥405B because of 192GB HBM3e fitting a single-GPU FP16 weight tensor. For mixed fleets serving multiple model sizes, $/M-token is workload-driven, not architecture-driven — see the matrix above and /cost-per-token for the workload-modelled breakdown.

At what model size does B200 start beating H100 on $/1M-tokens?

B200 starts winning on $/1M-tokens at roughly the 70B-200B model-size band in Q3 2026, but the cutover depends on batch and prefix-cache hit rate. Below 70B (e.g., 7B / 13B / 34B Llama-3-class), H100 wins decisively because vector-matrix compute is memory-bandwidth bound and B200's 192GB HBM3e doesn't translate into more tokens/second at small batches. At 70B-200B (Llama-3.1 70B, Mixtral 8x22B scaling, Yi-34B scaled), B200's FP4 path and 4th-gen tensor cores add ~1.8-2.2× throughput per dollar — the break-even point. At ≥405B (Llama-3.1 405B), B200 is structurally required: the FP16 weight tensor is ~810GB which exceeds two H100s' 160GB combined VRAM and forces tensor-parallel across 3+ H100s; a single B200's 192GB HBM3e serves the same model in FP16 with no inter-GPU communication. Run your exact (model, batch, sequence) tuple through /calculator and /cost-per-token to find the precise crossover — and GPU Forward Curve Pro for the latest forward-trend by model size.

How should a mixed B200 + H100 inference fleet be sized?

Match GPU to model size, not to $/hr. The convention from the cost-per-token matrix: small-model tier (≤13B FP16 / ≤34B BF16 high-batch) → H100 spot on Vast.ai at $1.49/hr, no Blackwell needed. Mid-model tier (70B-200B) → mixed fleet: H100 spot for elastic / prefix-cache-heavy paths, B200 reserved or spot for sustained-token-throughput paths where the FP4 tensor-core gain offsets the $/hr premium (3× $/hr × ~2× tokens/s ≈ parity, leaning B200 wins at 70B+). Large-model tier (≥405B) → B200 spot or reserved at $4.49/hr CoreWeave structurally required for single-GPU weight fit. A practical shape: ~60% H100 spot on small/mid traffic + ~30% B200 spot on 70B-405B traffic + ~10% B200 reserved for sustained baseline of the largest models. Sizes move with your exact workload shape — plug it into /reserved-instance-advisor and /product/gpu-forward-curve-pro.